Finding communities in linear time by developing the seeds
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人生时间轴英语作文Title: The Timeline of Life。
Life, a journey filled with twists and turns, highs and lows, joys and sorrows, is often depicted as a timeline—a linear progression from birth to death. This timeline serves as a reflection of our existence, markingsignificant milestones and shaping our experiences. Let's embark on a journey through the timeline of life.Birth (0 years):The journey begins with birth, the dawn of existence. It's a moment of pure innocence and vulnerability as we enter the world, greeted by the warmth of our loved ones and the promise of endless possibilities.Infancy and Childhood (0-12 years):During infancy and childhood, we embark on the journeyof self-discovery. Every moment is filled with wonder and curiosity as we explore the world around us, learning to crawl, walk, and eventually run. Our minds are like sponges, absorbing knowledge and experiences that shape our understanding of life.Adolescence (13-19 years):Adolescence marks the transition from childhood to adulthood—a period of rapid physical, emotional, and psychological growth. It's a time of identity formation, as we grapple with questions of who we are and who we want to become. We navigate the complexities of relationships, peer pressure, and societal expectations, all while striving for independence and autonomy.Young Adulthood (20-39 years):Young adulthood is a time of exploration and self-discovery. We pursue higher education, embark on careers, and build meaningful relationships. It's a phase characterized by passion, ambition, and a sense ofinvincibility as we chase our dreams and aspirations. Yet,it's also a period of uncertainty and self-doubt, as weface the realities of adulthood and the responsibilitiesthat come with it.Middle Adulthood (40-59 years):Middle adulthood brings a sense of stability and maturity. We settle into our careers, establish families, and take on leadership roles in our communities. It's atime of reflection, where we assess our achievements and reevaluate our priorities. We may experience midlife crises, grappling with questions of purpose and fulfillment, but ultimately emerge with a renewed sense of purpose and direction.Late Adulthood (60+ years):Late adulthood is a time of reflection and wisdom. We retire from our careers, but remain active members of society, contributing our knowledge and experience tofuture generations. It's a period marked by physicaldecline and health challenges, yet also by a deep sense of gratitude for a life well-lived. We cherish moments spent with loved ones, finding joy in the simple pleasures of everyday life.Conclusion:In the grand tapestry of life, our journey unfolds along the timeline, weaving together moments of joy and sorrow, growth and reflection. Each stage brings its own set of challenges and opportunities, shaping us into the individuals we are meant to be. As we traverse the timeline of life, let us embrace each moment with gratitude and resilience, for it is through the journey that we discover the true essence of our existence.。
如何找到自己位置作文英语英文回答:Finding Your Place in the World: A Guide to Self-Reflection and Growth。
In a constantly evolving and fast-paced world, it's natural to feel lost and uncertain about one's place in it. However, finding your unique path and purpose is crucialfor living a fulfilling life. This article will provide you with a comprehensive guide to embarking on a journey ofself-discovery and finding your true place in the world.1. Embrace Self-Reflection:The first step towards finding your place in the world is to engage in honest and introspective self-reflection. Take the time to ask yourself:What are my values, passions, and interests?What are my strengths, weaknesses, and aspirations?What makes me unique and special?Write down your answers and explore them thoroughly. Identify patterns and themes that emerge, which can helpyou gain a better understanding of who you are at your core.2. Explore Your Interests and Passions:Your interests and passions are often indicators ofwhat you were meant to do in life. Pursue activities that spark joy, challenge you, and make you feel alive. Try new things, experiment with different hobbies, and seek out experiences that ignite your curiosity. The more youexplore your interests, the closer you will come to discovering your true calling.3. Identify Your Strengths and Weaknesses:Knowing your strengths and weaknesses is essential forfinding your place in the world. Identify the areas where you excel and those where you need to improve. Use your strengths to your advantage and work on developing your weaknesses. Remember, everyone has both strengths and weaknesses, and it's embracing them that leads to personal growth.4. Seek Out Mentorship and Support:Surround yourself with people who believe in you and support your journey. Find mentors who can guide you, share their experiences, and provide valuable insights. Join support groups or communities where you can connect with like-minded individuals and share your struggles and triumphs.5. Set Realistic Goals and Take Action:Setting realistic goals and taking action are crucial for making progress towards finding your place. Break down your goals into smaller, manageable steps. Take one step at a time and celebrate your accomplishments along the way.Remember, progress is not always linear, so don't get discouraged by setbacks. Adjust your goals as needed and keep moving forward.6. Embrace Failure as an Opportunity for Growth:Failure is an inevitable part of life, and it should be embraced as an opportunity for learning and growth. Whenyou fail, ask yourself what you could have done differently. Analyze your mistakes and use them as stepping stones to improve. Remember, failure is not a definition of who you are, but rather a chance to become better.7. Stay True to Yourself:Throughout your journey of self-discovery, it'sessential to stay true to yourself. Don't try to fit into a mold that others expect of you. Follow your own path, evenif it's different from the norm. Embrace your unique qualities and never compromise your values.8. Be Patient and Persistent:Finding your place in the world takes time and effort. Be patient with yourself and don't get discouraged if you don't have all the answers right away. Keep exploring, learning, and growing. With persistence and dedication, you will eventually discover your true calling and find your place in the world.中文回答:如何找到自己位置,自省与成长的指南。
Making friends is a fundamental part of human social interaction,and it can greatly enhance ones life experience.Here are some tips and suggestions for writing an essay on making friends in English:1.Introduction:Begin your essay by introducing the topic of friendship and its importance in our lives.You might mention how friendships can provide support, companionship,and a sense of belonging.2.Importance of Friendship:Discuss why friendships are crucial.You could talk about the emotional,psychological,and social benefits of having friends.mon Interests:Suggest that finding friends with common interests is a good starting point.This can provide a natural basis for conversation and shared activities.4.Active Participation:Emphasize the importance of being active in social settings. Joining clubs,participating in community events,or engaging in group activities can be a great way to meet new people.munication Skills:Highlight the role of effective communication in forming friendships.Being a good listener,expressing oneself clearly,and showing empathy are key skills.6.Respect and Trust:Discuss the importance of respect and trust in building strong friendships.Friends should respect each others opinions,trust each other,and be reliable.7.Handling Conflicts:Address the inevitable conflicts that may arise in friendships and suggest ways to handle them constructively,such as open dialogue,compromise,and forgiveness.8.Maintaining Friendships:Offer advice on how to maintain friendships over time, including regular contact,showing care and concern,and being there for each other during difficult times.9.Cultural Sensitivity:If relevant to your audience,you might want to include a section on being sensitive to cultural differences when making friends,as this can enrich the friendship and broaden ones perspective.10.Conclusion:Summarize the main points of your essay and reiterate the value of friendships.You could end with a personal reflection or a call to action encouraging readers to seek out new friendships.Remember to use a variety of sentence structures and vocabulary to make your essay engaging.Additionally,providing reallife examples or anecdotes can make your points more relatable and persuasive.。
远处求道和近处求道的英语作文素材全文共3篇示例,供读者参考篇1Seeking Enlightenment: The Journey Within and BeyondAs a student, the pursuit of knowledge and enlightenment has been an integral part of my journey. From a young age, we are taught to look up to those who have achieved greatness, to admire the wise words of philosophers and scholars who have unlocked the mysteries of life. We are encouraged to seek wisdom from afar, to immerse ourselves in the teachings of those who have walked the path before us. However, as I have grown older, I have come to realize that the quest for enlightenment is not solely found in the words of the ancients or the teachings of distant masters, but also in the very world that surrounds us – a world that is often overlooked in our pursuit of lofty ideals.The allure of seeking enlightenment from afar is undeniable. There is a certain romance in the idea of embarking on a grand journey, of leaving the familiar behind and venturing into the unknown in search of truth. We are captivated by the tales ofseekers who have traversed vast distances, climbed towering mountains, and braved treacherous terrain, all in the name of finding that elusive spark of wisdom. We imagine ourselves as modern-day pilgrims, following in the footsteps of those who have gone before us, our minds and souls open to the teachings that await us at the end of our odyssey.Yet, as I have learned, the path to enlightenment is not always found in far-off lands or ancient texts. Sometimes, the greatest lessons are found in the most unexpected places, hidden within the everyday moments that we so often overlook. It is in the gentle whisper of the wind through the trees, the laughter of children at play, or the quiet stillness of a morning sunrise that we can find profound truths – truths that speak to the very essence of what it means to be human.In my own journey, I have discovered that seeking enlightenment from nearby sources can be just as rewarding, if not more so, than looking to distant horizons. It is in the simple act of truly listening to those around me, of opening my heart and mind to the stories and experiences of others, that I have found some of the most valuable lessons. Whether it is the wisdom of a beloved grandparent, the insights of a cherished friend, or the observations of a stranger encountered by chance,these moments of connection have the power to shift our perspectives and challenge our preconceived notions in profound ways.Moreover, seeking enlightenment from nearby sources allows us to ground ourselves in the present moment, to appreciate the beauty and complexity of the world that surrounds us. It is all too easy to become caught up in the pursuit of lofty ideals, to lose ourselves in the grand narratives of history and philosophy, forgetting that the truths we seek are often found in the most mundane of places. By embracing the world around us, by truly seeing and experiencing the richness of our immediate surroundings, we open ourselves to a deeper understanding of the interconnectedness of all things.Of course, this is not to say that we should abandon the pursuit of wisdom from afar altogether. The teachings of great thinkers and the insights of distant cultures have much to offer, and their contributions to our collective understanding cannot be understated. However, we must strike a balance, recognizing that the quest for enlightenment is not a linear path, but rather a journey that requires us to explore both the grand and the intimate, the distant and the nearby.As I continue on my own path of learning and growth, I am reminded of the importance of embracing both the far and the near in my pursuit of enlightenment. I must remain open to the wisdom of those who have come before me, to the teachings that have withstood the test of time and transcended borders and cultures. But I must also remain grounded in the present, attuned to the lessons that can be found in the world around me, in the stories and experiences of those I encounter on a daily basis.For it is in the delicate balance of these two approaches that true enlightenment can be found – a enlightenment that is not merely an intellectual pursuit, but a holistic embrace of the complexities of human existence. It is a journey that requires us to look both inward and outward, to seek wisdom in the grand narratives of history and philosophy, while also finding truth in the simple moments that make up the tapestry of our lives.In the end, the quest for enlightenment is not a destination, but a never-ending journey – a journey that demands our full presence, our willingness to learn from every source, and our ability to see the extraordinary in the ordinary. It is a journey that challenges us to expand our horizons while remaining grounded in the here and now, to seek wisdom from afar while alsoembracing the teachings that lie just beyond our doorstep. And it is a journey that holds the promise of not only personal growth, but also a deeper understanding of our place in this vast and wondrous universe.篇2The Paths of Knowledge: Seeking Wisdom from Afar and NearbyAs a student, the pursuit of knowledge has been a driving force behind my academic journey. From the moment I stepped foot into the hallowed halls of learning, I've been captivated by the endless expanse of wisdom that lies before us, waiting to be explored and understood. However, as I've delved deeper into the realms of scholarship, I've come to realize that the acquisition of knowledge is a multifaceted endeavor, one that can be approached from different angles – seeking wisdom from afar and seeking it from nearby.Seeking Knowledge from AfarThere is an undeniable allure to the notion of seeking knowledge from distant lands, of venturing beyond the familiar confines of our immediate surroundings and immersing ourselves in the rich tapestry of cultures, traditions, andperspectives that span the globe. This pursuit has long been revered as a rite of passage for scholars, a journey that not only broadens our intellectual horizons but also challenges us to confront our own biases and preconceptions.Throughout history, countless individuals have embarked on such quests, driven by an insatiable curiosity and a thirst for understanding. From the ancient Greek philosophers who traveled to Egypt and Persia to study the wisdom of those lands, to the intrepid explorers of the Age of Discovery who ventured into uncharted territories in search of knowledge, the pursuit of wisdom from afar has been a hallmark of human intellectual endeavor.In our modern era, the concept of seeking knowledge from afar has taken on new dimensions. With the advent of technology and the interconnectedness of our world, we are no longer bound by physical boundaries. The internet has become a vast repository of information, a virtual realm where the collective knowledge of humanity is at our fingertips. Online courses, virtual lectures, and collaborative platforms have opened up new avenues for learning, allowing us to tap into the expertise of renowned scholars and thinkers from around the globe without ever leaving the comfort of our homes.Yet, the allure of seeking knowledge from afar extends beyond the realm of the virtual. There is an undeniable richness and depth to be found in immersing oneself in unfamiliar cultures, in grappling with foreign languages, and in experiencing the world through the lens of diverse perspectives. It is through these encounters that we are challenged to question our assumptions, to confront our biases, and to cultivate a sense of empathy and understanding that transcends borders and boundaries.Seeking Knowledge NearbyWhile the allure of seeking knowledge from afar is undeniable, it would be a grave mistake to overlook the profound wisdom that can be found within our immediate surroundings. The pursuit of knowledge is not merely a matter of traversing great distances or delving into esoteric realms; it is also a journey of self-discovery, of recognizing the richness and complexity that exists within our own communities, our own cultures, and our own lived experiences.From the elders who carry the weight of generations of tradition and wisdom, to the unsung heroes who quietly shape the fabric of our societies, there is a wealth of knowledge to be gleaned from those who walk among us. It is in the stories of ourneighbors, the teachings of our mentors, and the insights of our peers that we can uncover profound truths about ourselves, our histories, and the intricate tapestry of human experience.Moreover, seeking knowledge nearby allows us to develop a deeper appreciation and understanding of our own roots, our own identities, and the unique perspectives that shape our worldviews. By immersing ourselves in the richness of our local communities, we are better equipped to navigate the complexities of a globalized world, to bridge divides, and to find common ground amidst the diversity of human experience.The Synthesis of PathsUltimately, the pursuit of knowledge is not a matter of choosing between seeking wisdom from afar or seeking it nearby; rather, it is the harmonious synthesis of these two paths that unlocks the true depth and breadth of human understanding. For it is in the interplay between the global and the local, the distant and the familiar, that we can cultivate a holistic and nuanced perspective, one that embraces the complexities of our world while remaining grounded in the wisdom of our immediate surroundings.As students, we are fortunate to inhabit a world where the boundaries of knowledge are constantly expanding, where thetools and resources at our disposal are more vast and varied than ever before. It is our responsibility, our duty, to embrace this wealth of opportunity, to venture forth into the unknown while remaining rooted in the rich tapestry of our own histories and identities.Whether we choose to embark on odysseys of cultural immersion, to delve into the virtual realms of global knowledge, or to seek wisdom within the confines of our own communities, the path to true understanding lies in the synthesis of these journeys. It is in the interplay between the distant and the nearby, the global and the local, that we can cultivate a depth of knowledge that transcends borders and boundaries, and unlock the full potential of our human intellect.As I stand at the crossroads of my academic journey, I am filled with a sense of awe and humility at the vastness of the knowledge that lies before me. The paths of seeking wisdom from afar and seeking it nearby stretch out before me, each offering its own unique insights and challenges. Yet, it is in the synthesis of these paths, in the harmonious interplay between the global and the local, that I believe the true essence of scholarship lies.With an open mind and a thirst for understanding, I am ready to embark on this journey, to embrace the complexities and contradictions that define the human experience, and to contribute my own voice to the ever-evolving tapestry of human knowledge. For it is in the pursuit of wisdom, whether from distant lands or from our own backyards, that we can truly unlock the full potential of our intellects and forge a deeper connection with the richness and diversity of our world.篇3The Pursuit of Knowledge: Seeking Wisdom from Far and NearAs a student, I find myself constantly grappling with the eternal quest for knowledge – a pursuit that has captivated the hearts and minds of scholars throughout the ages. From the ancient philosophers who pondered the nature of existence to the modern scientists who unravel the mysteries of the universe, the thirst for understanding has been an unwavering force, driving humanity ever forward.In this relentless journey, I have come to realize that the acquisition of knowledge is not a linear path, but rather a multifaceted endeavor that requires us to cast our gaze both farand near. It is a delicate balance between seeking wisdom from distant lands and embracing the lessons that lie within our immediate surroundings.Seeking Knowledge from AfarThere is an undeniable allure in the prospect of journeying to distant shores, immersing oneself in foreign cultures, and absorbing the teachings of those who have trodden paths vastly different from our own. The great thinkers and philosophers of antiquity understood this, embarking on epic voyages to quench their insatiable thirst for knowledge.Consider the legendary travels of Pythagoras, the ancient Greek mathematician and philosopher. It is said that he ventured deep into the heart of Egypt, where he studied under the tutelage of the esteemed priests, unraveling the secrets of geometry, astronomy, and the profound mysteries of the universe. His willingness to leave the familiar and embrace the unknown paved the way for groundbreaking discoveries that would shape the course of Western thought for centuries to come.Similarly, the Islamic Golden Age witnessed a remarkable flourishing of knowledge, as scholars from across the vast realms of the caliphate converged in great centers of learning, such asBaghdad and Cordoba. Here, they exchanged ideas, translated and preserved the works of ancient Greek and Roman philosophers, and pushed the boundaries of mathematics, astronomy, and medicine. This cross-pollination of cultures and the willingness to seek knowledge from distant lands laid the foundation for many of the advancements that would eventually catalyze the Renaissance in Europe.Even in our modern era, the pursuit of knowledge often necessitates a willingness to venture beyond one's borders. Renowned scholars and researchers frequently embark on expeditions, collaborating with their counterparts from around the globe, sharing insights, and forging new frontiers of understanding. The exchange of ideas transcends physical boundaries, enabling us to tap into the collective wisdom of humanity and push the boundaries of what is known.Seeking Knowledge from NearYet, as compelling as the allure of distant shores may be, we must not overlook the profound lessons that lie within our immediate surroundings. For knowledge is not a commodity to be acquired solely through grand voyages and epic journeys; it is a tapestry woven from the threads of our everyday experiences,our interactions with those around us, and our willingness to embrace the wisdom that resides in the familiar.Consider the ancient sages and philosophers who found enlightenment not in distant lands, but within the confines of their own communities. Socrates, the celebrated Athenian philosopher, spent his life engaging in dialogues with his fellow citizens, challenging their assumptions and encouraging them to question the very foundations of their beliefs. Through these seemingly ordinary exchanges, he unveiled profound truths about the nature of knowledge, ethics, and the human condition – truths that have reverberated through the ages.Similarly, the teachings of Confucius, the great Chinese philosopher, were rooted in the moral and ethical principles that governed the societal fabric of his time. He drew upon the wisdom of his ancestors, the traditions of his people, and the everyday experiences of those around him to craft a philosophy that would shape the cultural and intellectual landscape of East Asia for millennia to come.In our modern age, the importance of seeking knowledge from near is no less diminished. The richness of our local communities, the diversity of perspectives that surround us, and the wealth of knowledge embedded within our immediateenvironments offer invaluable opportunities for growth and understanding.As students, we need not look far to find sources of inspiration and wisdom. Our teachers, mentors, and peers –those who walk alongside us on this journey of learning –possess a wealth of knowledge and experiences that can enlighten and enrich our understanding. By engaging in open dialogue, embracing diverse viewpoints, and actively listening to the stories and insights of those around us, we can unlock a world of knowledge that might otherwise remain hidden.The Balance: Harmonizing the Far and NearUltimately, the true pursuit of knowledge lies in striking a harmonious balance between seeking wisdom from afar and embracing the lessons that lie within our immediate surroundings. It is a delicate dance, a constant interplay between the allure of the unknown and the familiarity of the known.As students, we must cultivate a spirit of curiosity and adventure, a willingness to venture beyond our comfort zones and explore the vast expanse of human knowledge. Yet, we must also remain grounded, rooted in the richness of our local communities and the wisdom that resides within our everyday experiences.By harmonizing these two paths, we can weave a tapestry of understanding that is both expansive and deeply rooted, drawing from the collective wisdom of humanity while remaining firmly anchored in the essence of our own lived experiences.In this way, the pursuit of knowledge becomes not merely an academic exercise, but a transformative journey – one that transcends the boundaries of time and space, connecting us to the timeless threads of human inquiry and enlightenment. It is a journey that challenges us to grow, to question, to embrace the unfamiliar, and to find profound truths in the most unexpected of places.As we embark on this lifelong quest, let us remember the words of the ancient Chinese philosopher Lao Tzu: "The journey of a thousand miles begins with a single step." Let us take that first step, with open hearts and minds, seeking wisdom from both the distant horizons and the familiar paths that lie before us, for it is in this harmonious balance that the true essence of knowledge resides.。
Finding community structure in very large networksAaron Clauset,1M.E.J.Newman,2and Cristopher Moore1,31Department of Computer Science,University of New Mexico,Albuquerque,New Mexico87131,USA2Department of Physics and Center for the Study of Complex Systems,University of Michigan,Ann Arbor,Michigan48109,USA 3Department of Physics and Astronomy,University of New Mexico,Albuquerque,New Mexico87131,USA(Received30August2004;published6December2004)The discovery and analysis of community structure in networks is a topic of considerable recent interestwithin the physics community,but most methods proposed so far are unsuitable for very large networksbecause of their computational cost.Here we present a hierarchical agglomeration algorithm for detectingcommunity structure which is faster than many competing algorithms:its running time on a network with nvertices and m edges is O͑md log n͒where d is the depth of the dendrogram describing the communitystructure.Many real-world networks are sparse and hierarchical,with mϳn and dϳlog n,in which case ouralgorithm runs in essentially linear time,O͑n log2n͒.As an example of the application of this algorithm we useit to analyze a network of items for sale on the web site of a large on-line retailer,items in the network beinglinked if they are frequently purchased by the same buyer.The network has more than400000vertices and2ϫ106edges.We show that our algorithm can extract meaningful communities from this network,revealinglarge-scale patterns present in the purchasing habits of customers.DOI:10.1103/PhysRevE.70.066111PACS number(s):89.75.Hc,05.10.Ϫa,87.23.Ge,89.20.HhI.INTRODUCTIONMany systems of current interest to the scientific commu-nity can usefully be represented as networks[1–4].Ex-amples include the internet[5]and the World Wide Web [6,7],social networks[8],citation networks[9,10],food webs[11],and biochemical networks[12,13].Each of these networks consists of a set of nodes or vertices representing, for instance,computers or routers on the internet or people in a social network,connected together by links or edges,rep-resenting data connections between computers,friendships between people,and so forth.One network feature that has been emphasized in recent work is community structure,the gathering of vertices into groups such that there is a higher density of edges within groups than between them[14].The problem of detecting such communities within networks has been well studied. Early approaches such as the Kernighan-Lin algorithm[15], spectral partitioning[16,17],or hierarchical clustering[18] work well for specific types of problems(particularly graph bisection or problems with well defined vertex similarity measures),but perform poorly in more general cases[19].To combat this problem a number of new algorithms have been proposed in recent years.Girvan and Newman[20,21] proposed a divisive algorithm that uses edge betweenness as a metric to identify the boundaries of communities.This al-gorithm has been applied successfully to a variety of net-works,including networks of email messages,human and animal social networks,networks of collaborations between scientists and musicians,metabolic networks,and gene net-works[20,22–30].However,as noted in[21],the algorithm makes heavy demands on computational resources,running in O͑m2n͒time on an arbitrary network with m edges and n vertices,or O͑n3͒time on a sparse graph(one in which m ϳn,which covers most real-world networks of interest). This restricts the algorithm’s use to networks of at most a few thousand vertices with current hardware.More recently a number of faster algorithms have been proposed[31–33].In[32],one of us proposed an algorithm based on the greedy optimization of the quantity known as modularity[21].This method appears to work well both in contrived test cases and in real-world situations,and is sub-stantially faster than the algorithm of Girvan and Newman.A naive implementation runs in time O(͑m+n͒n),or O͑n2͒on a sparse graph.Here we propose a different algorithm that performs the same greedy optimization as the algorithm of[32]and there-fore gives identical results for the communities found.How-ever,by exploiting some shortcuts in the optimization prob-lem and using more sophisticated data structures,it runs far more quickly,in time O͑md log n͒where d is the depth of the“dendrogram”describing the network’s community structure.Many real-world networks are sparse,so that m ϳn;and moreover,for networks that have a hierarchical structure with communities at many scales,dϳlog n.For such networks our algorithm has essentially linear running time,O͑n log2n͒.This is not merely a technical advance but has substantial practical implications,bringing within reach the analysis of extremely large works of107vertices or more should be possible in reasonable run times.As an example, we give results from the application of the algorithm to a recommender network of books from the on-line bookseller ,which has more than400000vertices and 2ϫ106edges.II.THE ALGORITHMModularity[21]is a property of a network and a specific proposed division of that network into communities.It mea-sures when the division is a good one,in the sense that there are many edges within communities and only a few betweenPHYSICAL REVIEW E70,066111(2004)them.Let A v w be an element of the adjacency matrix of the network;thusA v w=ͭ1if vertices v and w are connected,0otherwise,͑1͒and suppose the vertices are divided into communities such that vertex v belongs to community c v.Then the fraction of edges that fall within communities,i.e.,that connect vertices that both lie in the same community,is͚v w A v w␦͑c v,c w͚͒v w A v w=12m͚v wA v w␦͑c v,c w͒,͑2͒where the␦function␦͑i,j͒is1if i=j and0otherwise,andm=12͚v w A v w is the number of edges in the graph.This quan-tity will be large for good divisions of the network,in the sense of having many within-community edges,but it is not, on its own,a good measure of community structure since it takes its largest value of1in the trivial case where all verti-ces belong to a single community.However,if we subtract from it the expected value of the same quantity in the case of a randomized network,we do get a useful measure.The degree k v of a vertex v is defined to be the number of edges incident upon it:k v=͚w A v w.͑3͒The probability of an edge existing between vertices v and w if connections are made at random but respecting vertex de-grees is k v k w/2m.We define the modularity Q to beQ=12m͚v wͫA v w−k v k w2mͬ␦͑c v,c w͒.͑4͒If the fraction of within-community edges is no different from what we would expect for the randomized network, then this quantity will be zero.Nonzero values represent de-viations from randomness,and in practice it is found that a value above about0.3is a good indicator of significant com-munity structure in a network.If high values of the modularity correspond to good divi-sions of a network into communities,then one should be able tofind such good divisions by searching through the possible candidates for ones with high modularity.Whilefinding the global maximum modularity over all possible divisions seems hard in general,reasonably good solutions can be found with approximate optimization techniques.The algo-rithm proposed in[32]uses a greedy optimization in which, starting with each vertex being the sole member of a com-munity of one,we repeatedly join together the two commu-nities whose amalgamation produces the largest increase in Q.For a network of n vertices,after n−1such joins we are left with a single community and the algorithm stops.The entire process can be represented as a tree whose leaves are the vertices of the original network and whose internal nodes correspond to the joins.This dendrogram represents a hier-archical decomposition of the network into communities at all levels.The most straightforward implementation of this idea (and the only one considered in[32])involves storing the adjacency matrix of the graph as an array of integers and repeatedly merging pairs of rows and columns as the corre-sponding communities are merged.For the case of the sparse graphs that are of primary interest in thefield,however,this approach wastes a good deal of time and memory space on the storage and merging of matrix elements with value0, which is the vast majority of the adjacency matrix.The al-gorithm proposed in this paper achieves speed(and memory efficiency)by eliminating these needless operations.To simplify the description of our algorithm let us define the following two quantities:e ij=12m͚v wA v w␦͑c v,i͒␦͑c w,j͒,͑5͒which is the fraction of edges that join vertices in community i to vertices in community j,anda i=12m͚vk v␦͑c v,i͒,͑6͒which is the fraction of ends of edges that are attached to vertices in community i.Then,writing␦͑c v,c w͒=͚i␦͑c v,i͒␦͑c w,i͒,we have,from Eq.(4),Q=12m͚v wͫA v w−k v k w2m͚ͬi␦͑c v,i͒␦͑c w,i͒=͚iͫ12m͚v w A v w␦͑c v,i͒␦͑c w,i͒−12m͚vk v␦͑c v,i͒12m͚wk w␦͑c w,i͒ͬ=͚i͑e ii−a i2͒.͑7͒The operation of the algorithm involvesfinding the changes in Q that would result from the amalgamation of each pair of communities,choosing the largest of them,and performing the corresponding amalgamation.One way to en-visage(and implement)this process is to think of the net-work as a multigraph,in which a whole community is rep-resented by a vertex,bundles of edges connect one vertex to another,and edges internal to communities are represented by self-edges.The adjacency matrix of this multigraph has elements A ijЈ=2me ij,and the joining of two communities i and j corresponds to replacing the i th and j th rows and col-umns by their sum.In the algorithm of[32]this operation is done explicitly on the entire matrix,but if the adjacency matrix is sparse(which we expect in the early stages of the process)the operation can be carried out more efficiently using data structures for sparse matrices.Unfortunately,cal-culating⌬Q ij andfinding the pair i,j with the largest⌬Q ij then becomes time consuming.In our algorithm,rather than maintaining the adjacency matrix and calculating⌬Q ij,we instead maintain and update a matrix of value of⌬Q ij.Since joining two communities with no edge between them can never produce an increase inCLAUSET,NEWMAN,AND MOORE PHYSICAL REVIEW E70,066111(2004)Q,we need only store⌬Q ij for those pairs i,j that are joined by one or more edges.Since this matrix has the same support as the adjacency matrix,it will be similarly sparse,so we can again represent it with efficient data structures.In addition, we make use of an efficient data structure to keep track of the largest⌬Q ij.These improvements result in a considerable saving of both memory and time.In total,we maintain three data structures.(1)A sparse matrix containing⌬Q ij for each pair i,j of communities with at least one edge between them.We store each row of the matrix both as a balanced binary tree[so that elements can be found or inserted in O͑log n͒time]and as a max-heap(so that the largest element can be found in con-stant time).(2)A max-heap H containing the largest element of each row of the matrix⌬Q ij along with the labels i,j of the cor-responding pair of communities.(3)An ordinary vector array with elements a i.As described above we start off with each vertex being the sole member of a community of one,in which case e ij =1/2m if i and j are connected and zero otherwise,and a i =k i/2m.Thus we initially set⌬Q ij=ͭ1/2m−k i k j/͑2m͒2if i,j are connected,0otherwise,͑8͒anda i=k i2m͑9͒for each i.(This assumes the graph is unweighted;weighted graphs are a simple generalization[34].)Our algorithm can now be defined as follows.(1)Calculate the initial values of⌬Q ij and a i according to Eq.(8)and(9),and populate the max-heap with the largest element of each row of the matrix⌬Q.(2)Select the largest⌬Q ij from H,join the corresponding communities,update the matrix⌬Q,the heap H,and a i(as described below),and increment Q by⌬Q ij.(3)Repeat step2until only one community remains.Our data structures allow us to carry out the updates in step2quickly.First,note that we need only adjust a few of the elements of⌬Q.If we join communities i and j,labeling the combined community j,say,we need only update the j th row and column,and remove the i th row and column alto-gether.The update rules are as follows.If community k is connected to both i and j,then⌬Q jkЈ=⌬Q ik+⌬Q jk.͑10a͒If k is connected to i but not to j,then⌬Q jkЈ=⌬Q ik−2a j a k.͑10b͒If k is connected to j but not to i,then⌬Q jkЈ=⌬Q jk−2a i a k.͑10c͒Note that these equations imply that Q has a single peak over the course of the algorithm,since after the largest⌬Q be-comes negative all the⌬Q can only decrease.To analyze how long the algorithm takes using our data structures,let us denote the degrees of i and j in the reduced graph—i.e.,the numbers of neighboring communities—as͉i͉and͉j͉,respectively.Thefirst operation in a step of the algo-rithm is to update the j th row.To implement Eq.(10a),weinsert the elements of the i th row into the j th row,summingthem wherever an element exists in both columns.Since westore the rows as balanced binary trees,each of these͉i͉insertions takes O͑log͉j͉͒ഛO͑log n͒time.We then update the other elements of the j th row,of which there are at most ͉i͉+͉j͉,according to Eqs.(10b)and(10c).In the k th row,we update a single element,taking O͑log͉k͉͒ഛO͑log n͒time, and there are at most͉i͉+͉j͉values of k for which we have todo this.All of this thus takes O͉͑͑i͉+͉j͉͒log n͒time.We also have to update the max-heaps for each row andthe overall max-heap H.Reforming the max-heap corre-sponding to the j th row can be done in O͉͑j͉͒time[35]. Updating the max-heap for the k th row by inserting,raising, or lowering⌬Q kj takes O͑log͉k͉͒ഛO͑log n͒time.Since we have changed the maximum element on at most͉i͉+͉j͉rows, we need to do at most͉i͉+͉j͉updates of H,each of which takes O͑log n͒time,for a total of O(͉͑i͉+͉j͉͒log n). Finally,the update a jЈ=a j+a i(and a i=0)is trivial and can be done in constant time.Since each join takes O(͉͑i͉+͉j͉͒log n)time,the total run-ning time is at most O͑log n͒times the sum over all nodes of the dendrogram of the degrees of the corresponding commu-nities.Let us make the worst-case assumption that the degree of a community is the sum of the degrees of all the vertices in the original network comprising it.In that case,each ver-tex of the original network contributes its degree to all of the communities it is a part of,along the path in the dendrogram from it to the root.If the dendrogram has depth d,there are at most d nodes in this path,and since the total degree of all the vertices is2m,we have a running time of O͑md log n͒as stated.We note that,if the dendrogram is unbalanced,some timesavings can be gained by inserting the sparser row into theless sparse one.In addition,we have found that in practicalsituations it is usually unnecessary to maintain the separatemax-heaps for each row.These heaps are used tofind thelargest element in a row quickly,but their maintenance takesa moderate amount of effort and this effort is wasted if thelargest element in a row does not change when two rows areamalgamated,which turns out often to be the case.Thus wefind that the following simpler implementation works quitewell in realistic situations:if the largest element of the k throw was⌬Q ki or⌬Q kj and is now reduced by Eq.(10b)or (10c),we simply scan the k th row tofind the new largest element.Although the worst-case running time of this ap-proach has an additional factor of n,the average-case run-ning time is often better than that of the more sophisticated algorithm.It should be noted that the dendrograms generated by these two versions of our algorithm will differ slightly as a result of the differences in how ties are broken for the maximum element in a row.However,wefind that in prac-tice these differences do not cause significant deviations in the modularity,the community size distribution,or the com-position of the largest communities.FINDING COMMUNITY STRUCTURE IN VERY LARGE…PHYSICAL REVIEW E70,066111(2004) PURCHASING NETWORKThe output of the algorithm described above is precisely the same as that of the slower hierarchical algorithm of [32].The much improved speed of our algorithm,however,makes possible studies of very large networks for which previous methods were too slow to produce useful results.Here we give one example,the analysis of a copurchasing or “recom-mender”network from the online vendor .Amazon sells a variety of products,particularly books and music,and as part of their web sales operation they list for each item A the ten other items most frequently purchased by buyers of A .This information can be represented as a di-rected network in which vertices represent items and there is an edge from item A to another item B if B was frequently purchased by buyers of A .In our study we have ignored the directed nature of the network (as is common in community structure calculations ),assuming any link between two items,regardless of direction,to be an indication of their similarity.The network we study consists of items listed onthe Amazon web site in August 2003.We concentrate on the largest component of the network,which has 409687items and 2464630edges.The dendrogram for this calculation is of course too big to draw,but Fig.1illustrates the modularity over the course of the algorithm as vertices are joined into larger and larger groups.The maximum value is Q =0.745,which is high as calculations of this type go [21,32]and indicates strong com-munity structure in the network.The maximum occurs when there are 1684communities with a mean size of 243items each.Figure 2gives a visualization of the community struc-ture,including the major communities,smaller “satellite”communities connected to them,and “bridge”communities that connect two major communities with each other.Looking at the largest communities in the network,we find that they tend to consist of items (books,music )in simi-TABLE I.The ten largest communities in the network,which account for 87%of the vertices in the network.Rank Size Description1114538General interest:politics;art/literature;general fiction;human nature;technical books;how things,people,computers,societies work,etc.292276The arts:videos,books,DVDs about the creative and performing arts378661Hobbies and interests I:self-help;self-education;popular science fiction,popular fantasy;leisure;etc.454582Hobbies and interests II:adventure books;video games/comics;some sports;some humor;some classic fiction;some western religious material;etc.59872Classical music and related items61904Children’s videos,movies,music,and books71493Church/religious music;African-descent cultural books;homoerotic imagery 81101Pop horror;mystery/adventure fiction 91083Jazz;orchestral music;easy listening 10947Engineering;practical fashionFIG.1.The modularity Q over the course of the algorithm (the x axis shows the number of joins ).Its maximum value is Q =0.745,where the partition consists of 1684communities.FIG.2.A visualization of the community structure at maximum modularity.Note that some major communities have a large number of “satellite”communities connected only to them (top,lower left,lower right ).Also,some pairs of major communities have sets of smaller communities that act as “bridges”between them (e.g.,be-tween the lower left and lower right,near the center ).CLAUSET,NEWMAN,AND MOORE PHYSICAL REVIEW E 70,066111(2004)lar genres or on similar topics.In Table I,we give informal descriptions of the ten largest communities,which account for about87%of the entire network.The remainder is gen-erally divided into small,densely connected communities that represent highly specific copurchasing habits,e.g.,major works of sciencefiction(162items),music by John Cougar Mellencamp(17items),and books about(mostly female) spies in the American Civil War(13items).It is worth noting that because few real-world networks have community meta-data associated with them to which we may compare the inferred communities,this type of manual check of the ve-racity and coherence of the algorithm’s output is often nec-essary.One interesting property recently noted in some networks [30,32]is that when partitioned at the point of maximum modularity,the distribution of community sizes s appears to have a power-law form P͑s͒ϳs−␣for some constant␣,at least over some significant range.The Amazon copurchasing network also seems to exhibit this property,as we show in Fig.3,with an exponent␣Ӎ2.It is unclear why such a distribution should arise,but we speculate that it could be a result either of the sociology of the network(a power-law distribution in the number of people interested in various topics)or of the dynamics of the community structure algo-rithm.We propose this as a direction for further research.IV.CONCLUSIONSHere,we have described an algorithm for inferring com-munity structure from network topology which works by greedily optimizing the modularity.Our algorithm runs in time O͑md log n͒for a network with n vertices and m edges where d is the depth of the dendrogram.For networks that are hierarchical,in the sense that there are communities at many scales and the dendrogram is roughly balanced,we have dϳlog n.If the network is also sparse,mϳn,then the running time is essentially linear,O͑n log2n͒.This is consid-erably faster than most previous general algorithms,and al-lows us to extend community structure analysis to networks that had been considered too large to be tractable.We have demonstrated our algorithm with an application to a large network of copurchasing data from the on-line retailer .Our algorithm discovers clear communities within this network that correspond to specific topics or genres of books or music,indicating that the copurchasing tendencies of Amazon customers are strongly correlated with subject matter.Our algorithm should allow researchers to analyze even larger networks with millions of vertices and tens of millions of edges using current computing resources,and we look forward to seeing such applications.ACKNOWLEDGEMENTSThe authors are grateful to and Eric Prom-islow for providing the purchasing network data.This work was funded in part by the National Science Foundation under Grant No.PHY-0200909(A.C.,C.M.)and by a grant from the James S.McDonell Foundation(M.E.J.N.).[1]S.H.Strogatz,Nature(London)410,268(2001).[2]R.Albert and A.-L.Barabási,Rev.Mod.Phys.74,47(2002).[3]S.N.Dorogovtsev and J.F.F.Mendes,Adv.Phys.51,1079(2002).[4]M.E.J.Newman,SIAM Rev.45,167(2003).[5]M.Faloutsos,P.Faloutsos,and C.Faloutsos,-mun.Rev.29,251(1999).[6]R.Albert,H.Jeong,and A.-L.Barabási,Nature(London)401,130(1999).[7]J.M.Kleinberg,S.R.Kumar,P.Raghavan,S.Rajagopalan,and A.Tomkins,in Proceedings of the International Confer-ence on Combinatorics and Computing,Lecture Notes in Computer Science V 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如何实现人生的价值英语作文Life is a journey filled with countless opportunities and challenges. As we navigate through this journey, the question of how to achieve life's purpose often arises. This is a question that has been pondered by philosophers, thinkers, and individuals throughout history. The pursuit of finding meaning and purpose in our lives is a fundamental human endeavor, and the path to achieving it can be both complex and deeply personal.One key aspect of realizing life's purpose is self-reflection. It is essential to take the time to truly understand ourselves – our values, our strengths, our passions, and our aspirations. By engaging in this process of self-discovery, we can begin to identify the areas of our lives that bring us the greatest fulfillment and satisfaction. This introspection can help us clarify our priorities and align our actions with our core beliefs, ultimately guiding us towards a more meaningful and purposeful existence.Another important step in achieving life's purpose is to cultivate a sense of gratitude and appreciation for the present moment. It iseasy to become consumed by the pursuit of future goals and milestones, often overlooking the beauty and wonder that exists in the here and now. By practicing mindfulness and being present in our daily lives, we can find joy and meaning in the small moments that might otherwise go unnoticed. This gratitude can help us to savor the journey, rather than solely focusing on the destination.Furthermore, the pursuit of life's purpose often involves finding ways to contribute to the world around us. This can take many forms, from volunteering in our local communities to pursuing a career that aligns with our values and passions. By engaging in activities that positively impact others, we can experience a profound sense of purpose and fulfillment. When we feel that our actions are making a meaningful difference, it can provide a deep sense of satisfaction and motivate us to continue striving towards our goals.Additionally, the path to achieving life's purpose often involves embracing challenges and overcoming obstacles. Life is not a linear journey, and the road to fulfillment is often paved with setbacks and difficulties. However, it is in these moments of adversity that we have the opportunity to grow, learn, and develop resilience. By approaching challenges with a mindset of curiosity and determination, we can transform them into opportunities for personal growth and self-discovery.Moreover, the pursuit of life's purpose is often enhanced by the support and guidance of others. Surrounding ourselves with a network of individuals who share our values and aspirations can provide us with the encouragement, accountability, and inspiration needed to stay the course. Whether it's through mentorship, collaboration, or simply a supportive community, connecting with others who are on a similar journey can be a powerful catalyst for achieving our goals.Finally, it is important to recognize that the pursuit of life's purpose is an ongoing process, rather than a fixed destination. As we grow and evolve, our understanding of our purpose may shift and change. Embracing this fluidity and being open to the possibility of discovering new passions and directions can help us to remain adaptable and responsive to the ever-changing landscape of our lives.In conclusion, the quest to achieve life's purpose is a deeply personal and multifaceted endeavor. By engaging in self-reflection, cultivating gratitude, contributing to the world around us, embracing challenges, and connecting with supportive communities, we can navigate this journey with a greater sense of clarity, purpose, and fulfillment. Ultimately, the path to realizing our life's purpose is not a linear one, but rather a dynamic and evolving process that requires patience, courage, and a willingness to continuously learn and grow.。
英语八年级下第八单元主题作文范文Learning a new language is a challenging yet rewarding experience. As an eighth-grade student studying English, I have had the opportunity to delve deeper into the intricacies of this global language. The theme of our eighth unit has been particularly fascinating as it explores the various aspects of language and communication.One of the key aspects we have discussed is the role of language in shaping our understanding of the world around us. Language is not merely a tool for conveying information; it is a lens through which we perceive and interpret reality. The words we use, the idioms we employ, and the grammatical structures we choose all have the power to influence our thoughts and perspectives.For example, the way different languages categorize colors can have a significant impact on how individuals perceive and remember them. In some languages, there are distinct words for shades of blue that are considered separate colors, while in others, these shades may be grouped under a single term. This linguistic difference can lead tomeasurable differences in the way speakers of these languages identify and recall colors.Similarly, the presence or absence of certain grammatical features in a language can shape the way its speakers conceptualize time, space, and causality. In languages that have a grammatical distinction between the past, present, and future, speakers may be more inclined to think about time in a linear, sequential manner. In contrast, languages that lack these distinctions may encourage a more cyclical or holistic view of time.These linguistic differences not only reflect but also reinforce the cultural and cognitive diversity of our world. By studying and understanding the unique features of different languages, we gain a deeper appreciation for the richness and complexity of human experience.Another fascinating aspect of our unit's theme has been the role of language in shaping identity and social relationships. Language is a powerful tool for expressing and negotiating our individual and collective identities. The way we use language can convey information about our age, gender, social status, ethnicity, and cultural affiliations.For instance, the use of formal or informal language can signal thepower dynamics and social distance between speakers. In some cultures, the use of honorifics or specific pronouns is essential for conveying respect and maintaining hierarchical relationships. Mastering these linguistic nuances is crucial for effective communication and building meaningful connections with others.Additionally, language can be a powerful tool for asserting or challenging social norms and power structures. The use of inclusive or exclusive language, the choice of vocabulary, and the tone of communication can all contribute to the perpetuation or disruption of societal inequalities. By being mindful of the way we use language, we can actively participate in the ongoing process of social change and empowerment.Furthermore, our unit has explored the fascinating topic of language acquisition and the cognitive processes involved in learning a new language. We have learned about the critical period hypothesis, which suggests that there is a window of time during childhood when language learning is most efficient and effortless. We have also discussed the role of input, practice, and motivation in the language learning process.As an eighth-grade student, I have found these discussions particularly relevant to my own language learning journey. I have experienced the challenges and joys of mastering new vocabulary,grammatical structures, and pronunciation. I have also come to appreciate the cognitive benefits of bilingualism, such as enhanced problem-solving skills, increased mental flexibility, and improved memory.Throughout this unit, I have been inspired by the stories of individuals who have overcome language barriers and used their linguistic skills to connect with diverse communities, pursue their dreams, and make a positive impact on the world. These examples have reinforced my belief that language is not just a means of communication but a powerful tool for personal growth, cultural exchange, and social transformation.As I continue my language learning journey, I am determined to approach it with a sense of curiosity, diligence, and open-mindedness. I know that mastering a new language requires patience, dedication, and a willingness to make mistakes and learn from them. But I am confident that the rewards of this endeavor will be immense, both in terms of the practical benefits and the personal growth that it can foster.In conclusion, the theme of our eighth unit on language and communication has been a profound and eye-opening experience. Through our discussions and activities, I have gained a deeper understanding of the complex relationship between language,cognition, and culture. I am grateful for the opportunity to explore these topics and am committed to continuing my language learning journey, knowing that it will not only enhance my communication skills but also broaden my perspectives and enrich my understanding of the world around me.。
Unit1Part 1Answer key1.T2.F3.F4.F5.T6.T7.F8.T9.FCorrected False Statements: Possible answers2.Its continental climate is one of extremes .3.Americans are restless ,they like to be active.4.Americans are blunt, but not rude .7.Americans prefer family privacy and do not often have servant .they like to do things themselves。
9.Americans are noticeably informalAnswer key1.b2.c3.a4.a5.c6.b7.b8.b9.aAnswer key1.affordable2.seasonal3.applicant4.teacher5.cordiality6.harmlessAnswer keyPossible answers:Compound words with hyphens 1.music that is soft and easy to listen to 2.list of things that must be done 3.books that teach people how to help themselves4. someone who is always doing good things for others;Compound words without hyphens 1.A train trip that continues through the night ed by many people 3.ways of saying goodbye 4.areas by the sea 5.the home or houseAnswer key1.Polite2.impolite3.polite4.impolite5.polite6.impolite7.polite8.impolitePart 2Answer key1.stretching out in all directionrge size3.looks around4. character5.similar6.wetness7.has justarrived 8.small battles 9.sheriffs and policemen 10.shy 11.in the French wayAnswer key1.B2.C3.B4.A5.CAnswer key1.F,most inhabit cities2.T3.F Canada can get very hot4.F the history of Canada is not as bloody or violent as the United States5.T6.F the“wild”west was not part of Canada ,law came first ,settlement followed7.F Outward displays of emotion are not their style ,Americans are far more outgoing8.T9.F,Canadians are not anti-American 10.F,The buildings are designed in international styles. and the brand names in the supermarkets are all familiar.Answer keyPossible answer :1.people who are not resident 2.pills that stop anxiety 3.a protest against a war 4.a group of people who do not use violence 5.people who are not German 6.people who are not Mexican 7.someone who is against communism 8.people who do not vote ws that are against monopolies 10.not a paymentAnswer key1.decoration2.harmfulernment4.gloriousAnswer key1.environment2.imagination3.powerful4.moderation5.settlement6.movement7.mountainous8.mysteriousunit2part1Answer key1.e2.j3.g4.c5.d6.l7.k8.i9.h 10.b 11.a 12.fAnswer keyBeginning: First Event D F C G B A End :Last Event EPart 2Answer key1.592.353.17 billion,556 million4. 36.4 billion5.84%6.19967.4.7 billion8.7:00Answer key2Answer keyAnswer will vary. Possible answerage :“Grest people!great company!” he barks. Metaphor: The way Kim shouts is being compared to thebarking of a dog.age: Revenues jumped 18% last year; Metaphor: The way revenues rose is being compared to somethingthat jumps.age: ”Great company!Great company!” They chant back Metaphor: The way the employees respond isbeing compared to religious chanting.age: Kim…cavort in a mosh pit. Metaphor: The way Kim jumps around is being compared to leapingand prancing.age: Kim sliced costs. Metaphor: The way kim decreased costs is being compared to slicing a piece ofcheese with a knife.age: He storms about LG’s factories and offices; Metaphor: The way Kim visits his factories is beingcompared to a storm.Answer key1.e2.c3.a4.g5.d6.b7.fAnswer key1.Antonym2.Synonym3.Antonym4.Synonym5.Synonym6.Antonym7.Synonym8.Antonym9.Antonym 10.Synonym 11.Antonym 12.Antonym 13.Synonym 14.Synonym 15.AntonymAnswer key1.odd2.job3.underestimate4.innovation5.issuing6.goals7.perceivedAnswer key1.C2.C3.B4.D5.C6.A7.A8.B9.D 10.DUNIT 3Part 1Answer key2; Explanations will varyAnswer key1.c2.i3.e4.g5.n6.h7.b8.j9.k 10.l 11.f 12.d 13.a 14.mAnswer key1.702.stay-at-home mom3.immedietarger cities5.more6.207.someAnswer key1.with a grandparent2.with other non-relative3.12-17,Answer will vary.4.12-17;5.Answerwill vary6.answer will varypart 2answer keyColumn 1,SpecificColumn 2,General1.b2.e3.a4.d5.cAnswer key2Answer key1.advantageous2.exporter3.fictitious4.lourishing5.legal6.ntolerant7.cottage8.trickle9.invalid10.well-to-doAnswer key1.requires2.guarantee3.attitude4.requires5.obtain6.legal7.registers8.couple9.medical 10.requirements Answer key1.A2.D3.B4.C5.B6.A7.D8.BChapter 4Part 1一. 1.Early Diets: Nuts and Plants, Why Socrates Loved Olive Oil 2.Early Diets: Nuts and Plants3.Answers will vary二.It’s the traditional cuisine of poor, agrarian countries. If you eat a simple diet, you will feel great.三. 1.d 2.e 3.a 4.c 5.b四. 1.(title) A sure way to feel healthy and energetic is by eating simple foods free of sugar and fat.2.When people begin to make more money or gain exposure to different lifestyles they abandontraditional diets. Often this may lead to new illnesses.五. 1.c 2.f 3.a 4.e 5.b 6.d八. 1.B 2.D 3.C 4.D 5.C 6.CPart 2一. 2三. 1.C 2.D 3.A,B 4.D 5.B 6.C,B 7.C,B四. 1.F 2.F 3.O 4.O 5.O 6.F 7.F 8.O 9.F 10.F五. 1.natural resources 2.enchanted 3.inappropriate 4.taboo 5.acquiring 6.flock 7.inexpensive8.hippie 9.virtually 10.bargaining六. pensation 2.benefit munities 4.physical 5.hence 6.found 7.acquiring七. Circle A: annoyed, eager for adventure, believe they can contribute, take pictures, give away tokens CircleB: enchanted, skilled, playful, up-front, demanding, don’t believe tou rists can contribute, hate pictures, beg Middle part C: think money can help九. 1.They are the most popular tourist destinations, Europe, Answers will vary 2.Chart 1 lists the top 10countries according to number of tourists that visit, Chart 2 lists the top ten countries based on the money the earn from tourism 3.The amount of vacation days people living in that country have each year, 29, answers will varyChapter 5Part 1三. 1.C 2.A 3.C 4.A 5.B 6.A7.B 8.B 9.A四. 1.d 2.n 3.g 4.i 5.h 6.l 7.m 8.b 9.j 10.e 11.a 12.c 13.k 14.f一. Pattern 3二. I. Rohib, CambodiaII. V ietnamIII. Bangalore, IndiaIV. BoliviaV. Nallavadu a village in Pondicherry, India三. The image of the frog leaping over another represents how a community can go directly from being anagricultural to an information economy.四. 1.A 2.C 3.B 4.B 5.A 6.C 7.A8.C五. I. Location: Rohib,Cambodia1.Motorcycles are Internet-enabled. They carry information between remote village and centralcomputer hubs.II. Location: V ietnam1.knowledge-basedIII. Location: Bangalore, India1.interlocking programming shops2.call centers3.tech companiesIV. Location: Bolivia1.a rural radio station users the Internet to research informationV. Location: Nallavadu, a village in Pondicherry, India1.village’s telecentresed as a community alarm during the tsunami of 2004六. 1. motorcycles that can access the Internet 2. an economy based on services rather than goods 3.factories that produce goods on a large scale, in large quantities 4. an economy that is based oninformation and/or knowledge 5. the best course of action possible 6. people who have a high level of education; programmers who know a lot about technology; People who speak English 7. people with a high level of education, who know a lot about technology and speak English 8. a center where communication is connected to the Internet七. 1. medical 2. economy 3. global 4. benefits 5.via work puters 8.vehicle 9.data10.data 11.transmissionPart 3三. 1.mouse 2.adaptive 3.linear 4.table 5.directions 6.drag and drop 7.glossary 8.timer9.notepaperUnit 6Part 11 Possible answers : 1.Leopoldo Fernandez 2.decides to try something new 3.modernizes,addsconvenience. (Answers will vary.)2 1.giobalization 2.pizzeria 3.convenience 4.modernizing 5.management6.prospered7.specialties8.affordable9.mentality 10.maturing3 1.promoting the buying and selling of products e in and/or existing in many nations or countries3.estimated sales4. outlets5.a company with manyselling the same product 6.a quick and significant increase 7.a part of the business that not been explored yet 8.chains that are owned by individuals 9. a market that has potential to grow4 1.A 2.C 3.A 4.C 5.B 6.C5 1.C 2.D 3.BPart 21Possible answers : 1.in the 1930s Paris2.a writer, a womenWomen : wealthy ,forty , imposing, talkative3.the restaurant is more expensive than main narrator can afforded2 1.B 2.A 3.B 4.A 5.C 6.A 7.C 8.B 9.B 10.C3 Answers will vary.4 E, D, G, H, B, F, A, C5 1 . imposing 2.inclined 3.anticipated 4.enormous5. drama6.found7.inadequate6 Answers will vary.Unit 7Part 11 C2Possible answers: 1.the shared condition of being children 2.a person’s past, things someone has done in the past nd ruled by a prince 4.the main ideas that support a philosophy 5.someone who wishes to do good 6.someone who defends 7.view of things/the way someone sees things 8.one who starts new things 9.people who are common, not rich3 1.c 2.g 3.d 4.b 5.f 6.e4 1.enduring 2.influential 3.easil 4.political5.primarilyernmental7.confucian8.philosophical5 1.h 2.d 3.l 4.a 5.g 6.j 7.i 8.m 9.c 10.e 11.k 12.b 13.f6 1. T, He has reared in poverty and had no formal education2.F,Through diligent study, he educated himself and became a learned man.3.T,For two thousand years his ideas about personal conduct and morality permeated Chinese life andculture. Even today, his thoughts remain influential.4.T,He was the first major philosopher to state the Goden rule, “Do not do unto others that which you wouldnot have them do unto you”7 Answers will vary.Part 21 22 ment 2.eloquence 3.valor 4.overwheiming 5.take up the torch 6.atrocities pelling 8.sacrifice9.monitors 10.obstacles 11.conduct 12.atone3 1.c 2.a 3.d 4.b4 1.perception 2.expression mitment 4.repression 5.resparations 6.asistance 7.negotiations8.preparations 9.demilitarization 10.investments 11.decisions 12.servitude5 1.assistance 2.medical 3.role 4.civil 5.monitors6.violations7.promotion8.founded9.depressed 10.energyPart 31 after square 3Unit 8Part 11 1.cube 2.triangle 3.square 4.poygon 5.cone 6.spiral(helix)2 1.B 2.B 3.C 4.A 5.B 6.A 7.B 8.A 9.B 10.C3Possible answers: 1.wright had a lot of confidence in himself .He also believed he would be famous for his work. 2. Wright had assistants who were dedicated to working for him and who were very loyal to him .He was probably a good boss or a leader who inspired loyalty. 3. Wright thought he was too important to help the man with the roof, or he did not believe the problem was his fault. Perhaps he believed that practicality is not important. Wright believed architectural structures should expose people to their surrounding.4 1.contemorary 2.pioneer 3.obstinacy 4.conventional 5.fatigue,weariness 6.smirk5 1.A 2.B 3.C 4.C 5.A 6.BPart 21 1.B 2.B 3.C 4.A 5.C 6.C 7.C 8.A2 1.Her father was a professor from the United States and her mother, a singer, was a Mixteco Indian fromMexico2.Her album, Three of Life ,includes songs in three of the indigenous languages of Mexico, Zapotec,Nahuatl , and Mixteco.3…. Ajram began studying music under t he supervision of some of the finest teachers in her country.4.The 27-year-old rap artist sponsors workshops where poor kids can rap and break dance ,create graffiti,or learn how to be a DJ … To inspire kids in his old Bogota neighborhood, he gives away his own CDs appeared on the Sony Music label… He encourages children to mark their lives better by offering them workshops and giving away gifts.5.Don Popo himself started making music at the age of 13, two years after his own father was murdered. Itwas the only way this silent young man could express his feelings.Part 21 1.create 2.funds 3.demonstrate bl 5.academy 6.issues 7.sex 8.discrimination 9.sources 10.income2Answers will varyUnit 9Part 11.D2.C3.A4.B5.APart 21 1.three2.dialogue3.sad; Answers may include: The old man is lonely; The young waiter is unhappy about working late; Theold man is in despair and lonely.2 1.B 2.C 3.A 4.B 5.A 6.C3 Possible answers: 1.The old man must be very sad about something. He has problems. He is probablylonely or worried about his life. 2. The waiter who spoke last probably has money problems of his own, so he might think everyone is worried about money . 3. The younger waiter is probably only saying this because he is tired and wants to go home. He thinks that if the man had illed himself, he would get to close the restaurant and go home early. 4. The older wai ter probably doesn’t mind staying late. Perhaps he doesn’t have anyone waiting for him at home. Maybe he is as lonely as the old man. 5. The older waiter must substitute nada for other important words because he thinks that life for the old man and for himself is meaningless and offers them nothing, or nada.4 2Unit 10Part 11Possible answers: 1. Stan Mingo: a man with a “crime addiction” who is starting the meeting 2. Gary Johnson, executive director of HarbourLight, who believes crime is additive 3.Rich A.: spokesman for AA who has asked that his surname not be used and who believes that spiritual principles of AA may be adopted by other addictions. 4. Benedikt Fischer: associate professor of criminology and public health at the University of Toronto; research scientist with the Toronto-based Centre for Addiction and Mental Health, who believes that the term addiction is not “terribly helpful”. 5. George: has stolen money from family members. 6. Rich B: a crime and drug addict, who is feeling shame and guilt for the first time in years.2 1.g 2.m 3.j 4.o 5.a 6.d 7.l 8.e 9.h 10.f 11.n 12.i 13.k 14.c 15.b3Possible answers: 1.alcohol, unknown not revealed 2. 12crime, unknown not revealed 3. alibi, heroin4. addiction, it guides people to safety, someone who has washed up on shore4 1.B 2.A 3. B 4.C 5.A 6.C5 1. credits 2. draft 3.chaper 4.created 5.imposed 6.responds 7.pinciples 8.tradition 9.principle6 1. centre 2. Harbour 3. humour 4. neighbourhoodPart 21Possible answers: 1. The title tell us that the main character witnessed a murder. His eyes are brown, he doesn’t smile, his mother is thin, and he has a tic over his left cheekbone. He is nervous. He is thin and hasa moustache. He dresses neatly. His name is Mr. Struthers and he is important because he witnessed a crime.He only wants to speak to the lieutenant. 2. The narrator is the police officer who is interviewing Mr.Struthers. He is not an omniscient narrator. His name is Detective Cappeli. 3. Magruer is a police officer who had served on the force for a long time. 4. The victim is Lieutenant Anderson’s wife. She was mugged and stabbed.2 1. tic 2.underlings 3.taxpayer 4.Old Man 5.fear 6.accessory 7.deliberated3 1.nervously 2.carefully 3.personally 4.warily 5.suspiciusly6.abruptly7.quickly8.wearily4 Answers will vary.5 a, d, e6 1.23 2.Up 3.Federal2。
PHYSICAL REVIEW E84,036109(2011)Finding communities in linear time by developing the seedsAli A.Hakami Zanjani*and Amir H.Darooneh†Department of Physics,Zanjan University,P.O.Box45196-313,Zanjan,Iran (Received13October2010;revised manuscript received9May2011;published14September2011)We present an alternate method forfinding the communities in a complex network.We introduce two concepts named the seed of the community and the absorption power of the seed in complex networks.First,wefind the seeds and then develop them by considering their absorption power to achieve the communities.We compare the modularity and the computational complexity of this algorithm with some other existing methods,and we show that this algorithm is very fast and efficient in comparison with some recently fast algorithms.DOI:10.1103/PhysRevE.84.036109PACS number(s):89.75.Hc,89.70.Eg,89.75.FbI.INTRODUCTIONMany systems in nature,society,and technology that comprise many interacting parts with the ability of generating a new quality of macroscopic collective behavior are known as complex systems[1].One of the most convenient ways to represent complex systems is through networks or graphs[2,3]. The entities of the system are represented by the vertices of the graph and the interactions between the vertices are represented by the links.A diversity of real-world phenomena such as social relationships[4,5],biological and chemical interactions[6,7],spreading of viruses and diseases[8,9], earthquakes[10–12],the Internet and the World Wide Web [13–16],and linguistic phenomena[17–19]can be modeled with complex networks—for a review,see Ref.[20].Our knowledge about graphs can be obtained from studying both their relative quantities on a microscopic level(such as vertex degree,clustering coefficient of each vertex,etc.)and the quantities on a mesoscopic level(such as betweenness and modularity)[21].Thefirst quantities are related to the state of a single vertex or a few of them,but the second quantities depend upon the connections between a large set of vertices.Graphs which are representing real systems display large inhomogeneities,revealing a mesoscopic level of order and organization.There are many vertices with low degrees whereas some vertices have large degrees.On the other hand,the distribution of edges is not only globally, but also locally inhomogeneous,with high concentrations of edges within special groups of vertices,and low concentrations between these groups.We call this feature of real networks the community structure[22,23].Communities are,roughly speaking,groups of nodes that are densely connected to each other but only sparsely connected with the rest of the network. Detecting such communities is of interest,because the vertices of a community usually share common properties or play similar roles within the graph.We can classify the vertices according to their structural position in the communities. So,vertices in a special community which are sharing a large number of links with other nodes of that community may have important functions of control and stability within the community;vertices which are settled at the boundaries between communities play an important role of mediation *hakami.zanjani@znu.ac.ir†darooneh@znu.ac.ir and lead the relationships and exchanges between different communities[23].A large number of algorithms has been developed for detecting the communities.Spectral graph partitioning[24] and hierarchical clustering[25]are two traditional approaches that work quite reliably for specific types of problems,but perform poorly in more general cases[26].The method of Girvan and Newman[22],focused on the concept of betweenness,marked the beginning of a new era in thefield of community detection and opened this topic to physicists. Following this work of Girvan and Newman,a series of algorithms based on the idea of iteratively removing edges with a high centrality score have been proposed.Such methods use different measures of edge centrality,such as the random-walk betweenness,the current-flow betweenness[27],and the information centrality[28].Computational complexity[23]for the most of these algorithms is higher than O(n2),where n is the number of vertices,and this make them inapplicable for large networks. In order to deal with large networks,Newman developed a fast method directly based on the optimization of the modularity [29]with the time complexity O(n2).In a later paper,Clauset et al.improved this method by exploiting some shortcuts in the optimization problem and using more advanced data structures [30].The running time of this method is O(n log2n).Various other methods based on spectral analysis,statistical inference, and many different ideas can be found in the literature—for reviews see Refs.[3,23,31–34].Most of the current algorithms have some degree of stochas-ticity and the computational complexity of more accurate methods is often high.Here,we present an alternate method that is not only more accurate but also faster than most of the existing algorithms.It will be seen that the execution time and computational complexity of this algorithm is considerably low,in comparison with recently fast algorithms.The basic idea of some of the recently fast algorithms is the local optimization of a property,such asfitness function.These algorithms usually begin from a random vertex and extract a community,which has a maximum amount offitness;that is to say,adding a new node to this community,or eliminating any node from that will lower the amount of thefitness of that community.In the next step they choose another vertex,which is not yet assigned to any community,and extract another community and so forth[35,36].In some other methods the network to be studied is considered to be a circuit,where linksALI A.HAKAMI ZANJANI AND AMIR H.DAROONEH PHYSICAL REVIEW E 84,036109(2011)FIG.1.(Color online)(a)Zachary’s network of karate club members,a standard benchmark in community detection.There are four seeds found by the proposed algorithm,the sets {1,2},{6,7},{25,26},and {33,34}.(b)Lusseau’s network of bottlenose dolphins.There are six seeds found by the proposed algorithm,the sets {SN90,Upbang },{DN63,Knit },{Double,Zap },{Topless,Trigger },{Kringel,Oscar },and {Grin,Scabs }.Each dashed boundary indicates a developing seed and the label on a boundary is corresponded to a certain step of development process.are assigned a unit resistance and one particular pair of nodes which belong to different communities act as the unit voltage source and sink.A big voltage gap will happen between two communities and we will be able to separate the network into two communities [37].These algorithms have somedegreeFIG.2.(Color online)Overlapping communities for Zachary’s network of karate club members.(a)α=0.01.(b)α=0.005.of stochasticity,especially when the initial random nodes are lying at the boundary of the communities.In this paper,we introduce a unique algorithm that first finds the nodes which are settled in the heart of the communities and then develops these sets of nodes to obtain the communities.II.THE ALGORITHMThe basic assumption behind this algorithm is that,if two neighboring nodes have the maximum number of common neighbors,they most likely belong to the same community.Assuming this,we present a concept named the seed of the community.A seed is a set of nodes that make a complete subgraph,i.e.,each pair of this set are connected together;and each node of this set has the maximum number of common neighbors with any other node of this group,and the number of common neighbors between each node of this set and every node outside this group is less than or equal to this maximum value.For clarification,consider Zachary’s network of karate club members [see Fig.1(a)][38].We introduce a data table with four columns describing the network and we can extract the seeds of the communities from it.Each row of this table contains the indexes of two neighboring nodes,the number of links between them,and the number of common neighbors of these two nodes.Table I shows some parts of the data table for Zachary’s network.Now,we can extract the seeds from this table.It is obvious that nodes 1and 2construct a seed.If we complete the table,it will be seen that there are three other seeds in this network,the sets {6,7},{25,26},and {33,34}.After extracting the seeds,we develop them to achieve the communities.EachFINDING COMMUNITIES IN LINEAR TIME BY...PHYSICAL REVIEW E84,036109(2011) TABLE I.Some parts of the data table for Zachary’s network.pand q are the indexes of two neighboring nodes,w is the number oflinks between p and q,and NCN is the number of common neighborsof p and q.p q w NCN12171315141515121612171218131911111121121011311114131181112011122111321021172314241428132141321811220112221123110.. ..........9111 9312 93112 93313 93412.. ..........29310 293211 293411 302412 302711 303312 303413.. ..........seed absorbs its neighbors by different forces.We define the absorption power with the simple expressionf s=w bk s k n,(1)where w b is the number of links between the seed and its neighbor, k s is the sum of the degrees of the members of the seed s,and k n is sum of the degrees of the members of its neighbor n.Each neighbor of a seed may be a single node or another seed.We will henceforth represent a seed or a community with an indicator in brackets.For example,the seeds of Zachary’s network before developing are representedTABLE II.Absorption power of seeds for Zachary’s network.s is a seed and n is the neighbor of s,w b is the number of links between s and n,and f s is the absorption power of s;that is,the seed s absorbs its neighbor n with a force whose magnitude is defined by f s.s n w b f s [1]320.008 [1]420.013 [1]510.013 [1][6]20.010 [1]820.020 [1]910.008 [1]1110.013 [1]1210.040 [1]1310.020 [1]1420.016 [1]1820.040 [1]2020.027 [1]2220.040 [1]3110.010 [1]3210.007 [6][1]20.010 [6]510.042 [6]1110.042 [6]1720.125 [25]2410.033 [25]2810.042 [25]3220.056 [33]310.003 [33]920.014 [33]1010.017 [33]1410.007 [33]1520.034 [33]1620.034 [33]1920.034 [33]2010.011 [33]2120.034 [33]2320.034 [33]2420.013 [33]2710.017 [33]2810.009 [33]2910.011 [33]3020.017 [33]3120.017 [33]3220.011 by[1],[6],[25],and[33]corresponding to{1,2},{6,7}, {25,26},and{33,34}.To develop the seeds we use another table with four columns.Each row of this table contains theindex of the indicator of a seed s,a neighbor of this seed n,the number of links between the seed and its neighbor w b,andthe absorption power of the seed f s(see Table II).The maximum value of the absorption power in this table isf s max=0.125.Now the seeds in the rows with the maximum value of the absorption power absorb their neighbors and grow up.Therefore,the only developing seed will be[6]≡{6,7,17}. The other seeds will not grow up in this step.Now we refresh Table II and repeat this procedure to further develop the seeds. The results for some steps are shown in Fig.1(a).ALI A.HAKAMI ZANJANI AND AMIR H.DAROONEH PHYSICAL REVIEW E 84,036109(2011)10101010102(a)Number of NodesT i m e (s e c )1010101010102104(b)Number of NodesT i m e (s e c )FIG.3.(Color online)The execution time of the proposed algo-rithm as a function of the number of nodes in comparison with some existing fast methods:BGLL [41],WH [37],WCL [35],and LFK [36].(a)The used networks are some parts of “David Copperfield,”a novel by Charles Dickens.(b)The used networks are LFR benchmark graphs [42]with mixing parameter =0.2,average degree =15,maximum degree =50,exponent for the degree distribution =2,and exponent for the community size distribution =1.From the figure it can be seen that when there is no constraint,the process will be continued until all nodes will be swallowed up by only one community.To prevent this,we can use two constraints.First,we can terminate the process when there is no single node and all nodes are absorbed by the developed seeds.This happens in step c for Zachary’s network and there are four communities [see Fig.1(a)].Second,we can consider a threshold value αfor the maximum value of the absorption power.The process terminates when f s max becomes less than α.αis a real and positive parameter and we can control the size of the communities by varying its value.Indeed,αdetermines the number of iterations.For 0.0053<α<0.01,the process terminates after step c [see Fig.1(a)]for Zachary’s network and there are four communities which are labeled by the letter c in Fig.1(a);for 0.00165<α<0.0041,the equivalent set of [6]merges with the equivalent set of [1]and the equivalent set of [25]merges with the equivalent set of [33],and so there are two communities which are labeled by the letter d in Fig.1(a);for α<0.00164there is only one community,the network itself.As another example we have found communities for the network of bottlenose dolphins living in Doubtful Sound,101010105Number of NodesM a x i m u m M o d u l a r i t yFIG.4.(Color online)The maximum modularity of the proposed algorithm as a function of the number of nodes in comparison with BGLL’s method.The used networks are LFR benchmark graphs [42]with mixing parameter =0.2,average degree =15,maximum degree =50,exponent for the degree distribution =2,and exponent for the community size distribution =1.analyzed by Lusseau [39].As it is shown in Fig.1(b)there are six seeds in this network.For 0.0039<α<0.0055,the process terminates after step c [see Fig.1(b)]for the dolphins’network and there are six communities which are labeled by the letter c in Fig.1(b);for 0.0017<α<0.0027,the equivalent set of [SN90]merges with the equivalent set of [DN63],the equivalent set of [Double]merges with the equivalent set of [Topless],and the equivalent set of [Grin]merges with the equivalent set of [Kringel],and so there are three communities which are labeled by the letter d in Fig.1(b);for 0.0004<α<0.0016there are two communities which are labeled by the letter e in Fig.1(b);for α<0.0003there is only one community,the network itself.Then we can see the hierarchical community structure in these networks by this method.In this method we assumed that each node of the network belongs to only one community,i.e.,the communities are not overlapping;but in developing process it is possible for two or more seeds to exist which absorb a certain node by the same force and when the absorption power is maximum;in this case we prevent the node from absorbing into any of the seeds and we put it aside in the current iteration.By making some changes in the algorithm we can find the overlapping communities in a network.After extracting the seeds,we can develop each of them independent of the other seeds.In this case,all neighbors of a seed are considered as single nodes.In each iteration,first we find the maximum value of the absorption power for each seed individually,then each seed absorbs its neighbors where the absorption power of the seed on them is at a maximum value.The development process of each seed terminates when the value of the absorption power of that seed on all its neighbors becomes less than the considered threshold value α.In this case,communities may overlap and the overlapping regions depend on the value of α(see Fig.2).As it is shown in Fig.2(b),nodes 3,9,10,FINDING COMMUNITIES IN LINEAR TIME BY ...PHYSICAL REVIEW E 84,036109(2011)and 31are shared between the two main groups of Zachary’s network.Most of the running time of this algorithm is concentrated on the first part,for extracting the seeds.For each node i ,there are k i comparisons to find the neighbor (neighbors)which has (have)the maximum number of common neighbors with the node i and therefore there will be at most ni =1k i comparisons,where k i is the degree of node i and n is the number of nodes.On the other hand,12 ni =1k i =m ,where m is the number of links.Consequently,the time complexity of this method is O (m ).III.TESTING THE ALGORITHMThe written human language is one of the most important examples of a complex system that exhibits small-world and scale-free behavior [17].The co-occurrence of words in sentences can be described in terms of a graph of word interactions [18].If we consider a text as a network,the words will be the vertices,and if two words are neighbors,then they will be linked.According to Heaps’law [40]the number of different words found in a text increases as a power law with the increasing text size,i.e.,the number of links in a network of words is a function of the number of nodes,and therefore if we use a community detection algorithm for a network of words,the computational complexity of the algorithm which is usually a function of the number of nodes and the number of links will be a function of only the number of nodes.So we used the networks of words to compare the complexity of the proposed algorithm with the complexity of some existing fast algorithms.There are some qualitative comparisons for community detection algorithms in literature (for reviews see Refs.[3,23]),and we compare our method with four of the recently reported fast algorithms [35–37,41].We implemented the proposed algorithm by the C++programming language running on a single core Intel(R)Celeron(R)CPU 2.40GHz of a computer with 1GBN o r m a l i z e d Mu t u a l I n f o r m a t i o nMixing Parameter μMixing Parameter μFIG.5.(Color online)Test of our algorithm on the LFR bench-mark.The number of nodes is N =1000,γis the exponent for the degree distribution,βis the exponent for the community size distribution,and k is the average degree.memory.We used some parts of “David Copperfield,”a novel by Charles Dickens.We have also tested our method on LFR benchmark graphs [42]with mixing parameter =0.2,average degree =15,maximum degree =50,exponent for the degree distribution =2,and exponent for the community size distribution =1.The results are shown in Fig.3.As we see in Fig.3,the execution time of the proposed method is too low in comparison with three of the methods (WH [37],WCL [35],and LFK [36]);on the other hand,the slope of the graph of this method is almost identical to the slope of the graph that corresponds to BGLL’s [41]method,which has the lowest computational complexity among the methods.The quality of the partitions resulting from a method is often measured by the modularity [22,27].In the case of weighted networks,the modularity is defined as [43]Q =12mi,j A ij −k i k j 2mδ(c i ,c j ),(2)where A ij is the weight of the link between vertex i and vertexj ,k i is the sum of the weights of the links attached to i ,c i is the community which the vertex i belongs to it,m is the sum of the weights of the links,and the δfunction δ(c i ,c j )is 1if c i =c j ,that is,i and j belong to the same community and 0otherwise.The modularity of the partitions resulting from our method depends on the resolution of the method,which is tuned by the parameter α.We have compared the maximum modularity obtained from our method with the modularity of partitions obtained from BGLL’s method,which is based on modularity optimization.The results are shown in Fig.4.Although BGLL’s method is a fast and efficient algorithm,it is not able to detect overlapping communities,whereas our method can find them (see Fig.2).We have also used normalized mutual information [44]to measure the similarity between the predetermined partitions by the LFR benchmark and the communities which have beenN o r m a l i z e dM u t u a l I n f o r m a t i o nMixing Parameter μMixing Parameter μFIG.6.(Color online)Test of our algorithm on the LFR bench-mark.The number of nodes is N =5000,γis the exponent for the degree distribution,βis the exponent for the community size distribution,and k is the average degree.ALI A.HAKAMI ZANJANI AND AMIR H.DAROONEH PHYSICAL REVIEW E84,036109(2011)found by our algorithm.The results are shown in Figs.5and6. 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