Abstract
With the continuous development of artificial intelligence technology,online Internet technology has accelerated the popularity of social networking applications.The network contains a huge amount of data,it also contains valuable data information and network topology features,including personal information and individual individuals.Relationship is an important part of social networks.However,how to find useful information in massive data and predict implicit links in the network has become an important research topic.Therefore, information analysis for social networks has become a hot topic.The main task of link prediction is to predict the future possible links through existing links in the current network. The link prediction problem in social networks has great practical application value and attracts more and more researchers'attention and research.
Due to the existing link prediction algorithm,there is a problem that the link weight is single,and it cannot be applied to a complex type of heterogeneous social network.Therefore, in this paper,based on heterogeneous social networks with weighted types,the concept of node weights is introduced into the link weighting process between users and users on the basis of constructing the heterogeneous network structure,which quantifies the time impact of users on the location signing behavior.At the same time,the location and user links,as well as the location and location links are weighted.Finally,a mixed weighted link prediction algorithm for heterogeneous social networks based on user location is proposed.In addition, according to the user's behavioral relationship to location access,the user's preference for location check-in behavior is regarded as a latent property,and the user-user link is weighted by bias,and an offset-weighted user link algorithm is proposed.
Finally,this paper selects the real data set and conducts experiments and analysis on mixed weighted link prediction algorithm and biased weighted user link algorithm in heterogeneous networks.The results show that the mixed weighted link prediction algorithm for heterogeneous social networks based on user location has obvious prediction effect,and the biased user link algorithm has obvious prediction effect in the case of sparse data.
Key words:social network,heterogeneous,link prediction,weighting
目录
摘要.....................................................................................................................................................................I Abstract..............................................................................................................................................................II 目录..................................................................................................................................................................III 第1章绪论 (1)
1.1研究背景与意义 (1)
1.2国内外研究现状 (2)
1.3研究内容与本文结构 (4)
1.3.1研究内容 (4)
1.3.2本文组织结构 (4)
第2章社交网络链路预测分析研究基础 (6)
2.1社交网络分析模型 (6)
2.1.1链路的形式化表示 (7)
2.2基于网络结构相似性的链路预测算法 (8)
2.2.1基于局部相似性算法 (8)
2.2.2基于全局信息 (10)
2.2.3基于随机游走的相似性算法 (11)
2.3其他链路预测算法 (13)
2.3.1最大似然估计 (13)
2.3.2概率关系模型 (14)
2.4链路预测算法实验与评测 (15)
2.5本章小结 (16)
第3章基于异构网络的混合加权链路预测算法 (17)
3.1异构网络结构 (17)
3.2异构网络链路加权 (19)
3.2.1用户与用户链路加权 (20)
3.2.2用户和位置之间的链路加权 (22)
3.2.3位置与位置之间的链路加权 (25)
3.3构造概率转换矩阵 (27)
3.4异构加权网络上的随机游走算法 (28)
3.5实验与结果分析 (31)
3.5.1实验数据集与处理 (31)
3.5.2算法效果分析 (32)
3.5.3权衡参数影响 (34)
3.5.4实验结果与分析 (36)
3.6本章小结 (39)
第4章基于位置属性的用户加权链路预测 (40)
4.1用户链路预测算法 (40)
4.1.1相关定义 (40)
4.1.2原理和方法 (42)
4.2实验结果与分析 (45)
4.3本章小结 (45)
第5章总结与展望 (47)
5.1工作总结 (47)
5.2展望 (47)
参考文献 (49)