A Clustering-based Link Prediction Method in Social Networks

Link prediction is an important task in social network analysis, which also has applications in other domains like, recommender systems, molecular biology and criminal investigations. The classical methods of link prediction are based on graph topology structure and path features but few consider clustering information. The cluster in graphs is densely connected group of vertices and sparsely connected to other groups. Actually, the clustering results contain the essential information for link prediction, and these vertices common neighbors may play different roles depending on if they belong to the same cluster. Based on this assumption and characteristics of the common social networks, in this paper, we propose a link prediction method based on clustering and global information. Our experiments on both synthetic and real-world networks show that this method can improve link prediction accuracy as the number of cluster grows.

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