Graph kernel based link prediction for signed social networks

By revealing potential relationships between users, link prediction has long been considered as a fundamental research issue in singed social networks. The key of link prediction is to measure the similarity between users. Existing works use connections between target users or their common neighbors to measure user similarity. Rich information available for link prediction is missing since use similarity is widely influenced by many users via social connections. We therefore propose a novel graph kernel based link prediction method, which predicts links by comparing user similarity via signed social network's structural information: we first generate a set of subgraphs with different strength of social relations for each user, then calculate the graph kernel similarities between subgraphs, in which Bhattacharyya kernel is used to measure the similarity of the k-dimensional Gaussian distributions related to each k-order Krylov subspace generated for each subgraph, and finally train SVM classifier with user similarity information to predict links. Experiments held on real application datasets show that our proposed method has good link prediction performances on both positive and negative link prediction. Our method has significantly higher link prediction accuracy and F1-score than existing works.