Link Prediction in Directed Signed Networks Using the Commute Time of Random Walk
In this paper, we studied the problem of link prediction in directed signed social networks.The relationships of these networks can be either positive (friendly) or negative (hostile) and the relationships are directed.We extended and generalized the commute time similarity of standard random walk theory in undirected unsigned networks to directed signed networks.We introduced and defined a Laplacian matrix in directed signed networks and proved that its Moore-Penrose pseudoinverse was a legal kernel to compute the nodes” similarity.Motivated by the method of collaborative filtering, we proposed a link prediction method in directed signed networks to predict the link”s sign and direction based on the defined nodes” similarity.We carried out experiments on two datasets from Epinions and Slashdot.Experimental results indicated that we got significant perfomance in temps of sign accuracy and AUC in the two real datasets.
Directed Signed Network Link Prediction Collaborative Recommendation Random Walk Commute Time
HU Baofang WANG Hong YUAN Weihua
School of Information Science and Engineering,Shandong Normal University,Jinan 250014,China;Departme School of Information Science and Engineering,Shandong Normal University,Jinan 250014,China;Shandong
国内会议
金华
英文
1-11
2015-10-30（万方平台首次上网日期，不代表论文的发表时间）