Exploiting Cluster-based Meta Paths for Link Prediction in Signed Networks

Many online social networks can be described by signed networks, where positive links signify friendships, trust and like; while negative links indicate enmity, distrust and dislike. Predicting the sign of the links in these networks has attracted a great deal of attentions in the areas of friendship recommendation and trust relationship prediction. Existing methods for sign prediction tend to rely on path-based features which are somehow limited to the sparsity problem of the network. In order to solve this issue, in this paper, we introduce a novel sign prediction model by exploiting cluster-based meta paths, which can take advantage of both local and global information of the input networks. First, cluster-based meta paths based features are constructed by incorporating the newly generated clusters through hierarchically clustering the input networks. Then, the logistic regression classifier is employed to train the model and predict the hidden signs of the links. Extensive experiments on Epinions and Slashdot datasets demonstrate the efficiency of our proposed method in terms of Accuracy and Coverage.