基于随机游走的带有属性网络的链接预测  

Link Prediction in Networks with Node Attributes Based on Random Walks Algorithm

作　　者：陈永祥[1] 陈崚[1,2] 

机构地区：[1]扬州大学信息工程学院计算机系,扬州225009 [2]南京大学计算机软件新技术国家重点实验室,南京210093

出　　处：《计算机科学》2016年第6期199-203,213,共6页Computer Science

基　　金：国家自然科学基金(61379066;61070047;61379064;61472344);国家973项目(2012CB316003);江苏省自然科学基金(BK20130452;BK2012672;BK2012128;BK20140492;BK2010318;BK201010134);江苏省教育部门自然科学基金(12KJB520019;13KJB520026;09KJB20013);江苏省研究生培养创新工程项目(CXZZ13_0172)资助

摘　　要：链接预测的问题是复杂网络分析中的一个重要研究领域,已经在社会学、生物信息学、信息科学以及计算机科学等领域得到了广泛的应用。提出了一个顶点具有属性的网络链接预测的随机游走算法。在此算法中,根据顶点和属性的链接相似度定义了每一条边上的传播概率。并将顶点的属性相似度作为顶点间的相似度的初值,然后根据传输概率在网络中以随机游走的方式进行传播和更新,最终得到顶点间的相似度作为链接预测的结果得分。实验结果显示,提出的算法在顶点带属性的网络中取得了比其他算法更精确的预测结果。Link prediction is an important area in complex network analysis. It is widely used in various domains such as sociology,anthropology,information science and computer science. A link prediction algorithm was proposed based on link similarity score propagation by a random walk in networks with node attributes. In the algorithm, each link in the network is assigned a transmission probability according to the similarity of the attributes on the nodes connected by taking a link. The similarity of the attributes on the nodes is also treated as the initial value of their link similarity. The link similarity between the nodes is then propagated by the random walk in the network according to their transmission probability. At last, the link similarity is just the final score of link prediction. Our experimental results show that the proposed algorithm can obtain more accurate results than other algorithms in the networks with node attributes.

关 键 词：链接预测 属性 随机游走 复杂网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]