张昱,高克宁,于戈.一种融合节点属性信息的社会网络链接预测方法[J].计算机科学,2018,45(6):41-45
一种融合节点属性信息的社会网络链接预测方法
Method of Link Prediction in Social Networks Using Node Attribute Information
投稿时间:2017-03-11  修订日期:2017-06-18
DOI:10.11896/j.issn.1002-137X.2018.06.007
中文关键词:  链接预测,社会网络,社会-属性网络,社会节点,属性节点
英文关键词:Link prediction,Social network,Social-attribute network,Social node,Attribute node
基金项目:本文受教育部基本科研业务费项目青年教师科研启动基金(N151603001),辽宁省科技攻关项目博士启动基金(201601026)资助
作者单位
张昱 东北大学计算机科学与工程学院 沈阳110819 
高克宁 东北大学计算机科学与工程学院 沈阳110819 
于戈 东北大学计算机科学与工程学院 沈阳110819 
摘要点击次数: 267
全文下载次数: 201
中文摘要:
      随着大规模社会网络的发展,链接预测成为了一个重要的研究课题。研究了在社会网络中融合节点属性信息进行链接预测,在传统的社会-属性网络图模型的基础上,将节点属性的类别这一重要参量加入到网络构建中。基于此,提出了一系列为网络中不同类型的连边分配边权重的方法,最后通过随机游走的方法进行网络链接的预测。实验表明,所提链接预测方法相比同类方法有明显的效果提升。
英文摘要:
      With the development of large social networks,link prediction has become an important research subject.The link prediction problem in social networks using rich node attribute information was studied in this paper.Based on attribute-augmented social network model,which means rebuilding an augmented network by adding additional nodes with each node corresponding to an attribute,called social-attribute network,the classification of node attributes was added to the model as an important parameter.Several methods of assigning weights for different kinds of links were proposed.Then a random walk method was used for link prediction in the network.Experimental results reveal that this method has better performance compared with other similar methods.
查看全文  查看/发表评论  下载PDF阅读器