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基于LDA主题模型的社会网络链接预测
  • ISSN号：1672-3961
  • 期刊名称：《山东大学学报：工学版》
  • 时间：0
  • 分类：TP301[自动化与计算机技术—计算机系统结构;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[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
  • 相关基金：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）;江苏省六人人才高峰计划资助项目（2013-XXRJ-018）
作者： 卢文羊[1], 徐佳一[1], 杨育彬[1]
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, social network, link prediction, Latent Dirichlet Allocation, network evolution, topic model
中文摘要：

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。

英文摘要：

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.

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期刊信息
  • 《山东大学学报：工学版》
  • 北大核心期刊（2011版）
  • 主管单位:教育部
  • 主办单位:山东大学
  • 主编：李术才
  • 地址：山东济南市经十路17923号
  • 邮编：250061
  • 邮箱：xbgxb@sdu.edu.cn
  • 电话：0531-88396452
  • 国际标准刊号：ISSN：1672-3961
  • 国内统一刊号：ISSN：37-1391/T
  • 邮发代号:24-221
  • 获奖情况:
  • 国内外数据库收录:
  • 美国化学文摘（网络版）,波兰哥白尼索引,美国剑桥科学文摘,中国中国科技核心期刊,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）
  • 被引量:6258