基于LDA主题模型的社会网络链接预测  

LDA-based link prediction in social network

作　　者：卢文羊 徐佳一[1] 杨育彬[1] 

机构地区：[1]南京大学计算机软件新技术国家重点实验室,江苏南京210023

出　　处：《山东大学学报（工学版）》2014年第6期26-31,共6页Journal of Shandong University（Engineering Science）

基　　金：教育部新世纪优秀人才计划资助项目(NCET-11-0213);国家自然科学基金资助项目(61273257;61035003;61021062);江苏省六大人才高峰计划资助项目(2013-XXRJ-018)

摘　　要：针对传统社会网络链接预测方法忽视节点文本内容的问题,提出一种基于潜在狄利克雷分配(Latent Dirichlet Allocation,LDA)主题模型的协作演化链接预测算法。算法利用LDA模型,对节点的文本内容进行分析,提取出每个节点的主题分布向量,利用分布向量的点积来衡量节点文本的相似性;然后将节点文本内容相似性矩阵与节点邻接矩阵相加,在此基础上计算节点之间的相似性;最后选取相似性最高的k个节点作为预测结果。实验结果表明该算法在网络图稀疏的情况下有较好的效果。To address the problem of ignoring the text contents of nodes in social network link prediction methods,a La-tent Dirichlet Allocation（LDA）-based collaborative evolutionary link prediction algorithm was proposed.The algorithm used LDA model to analyze the text content and abstracted a topic distribution vector for each node;The product of the topic distribution vectors was adopted to measure the similarity between the nodes′contents;Afterwards,the content similarity matrix was added to the adjacency matrix and the similarities between the nodes were computed consequently;At last,k most similar nodes were selected as the prediction result.The experimental results showed that the proposed algorithm achieved good prediction performance in sparse networks.

关 键 词：社会网络 链接预测 潜在狄利克雷分配 网络演化 主题模型 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]