Social Influence Based Clustering of Heterogeneous Information Networks

Social networks continue to grow in size and the type of information hosted. We witness a growing interest in clustering a social network of people based on both their social relationships and their participations in activity based information networks. In this paper, we present a social inﬂuence based clustering framework for analyzing heterogeneous information networks with three unique features. First, we introduce a novel social inﬂuence based vertex similarity metric in terms of both self-inﬂuence similarity and co-inﬂuence similarity. We compute self-inﬂuence and coinﬂuence based similarity based on social graph and its associated activity graphs and inﬂuence graphs respectively. Second, we compute the combined social inﬂuence based similarity between each pair of vertices by unifying the self-similarity and multiple co-inﬂuence similarity scores through a weight function with an iterative update method. Third, we design an iterative learning algorithm, SI-Cluster, to dynamically reﬁne the K clusters by continuously quantifying and adjusting the weights on self-inﬂuence similarity and on multiple co-inﬂuence similarity scores towards the clustering convergence. To make SI-Cluster converge fast, we transformed a sophisticated nonlinear fractional programming problem of multiple weights into a straightforward nonlinear parametric programming problem of single variable. Our experiment results show that SI-Cluster not only achieves a better balance between self-inﬂuence and co-inﬂuence similarities but also scales extremely well for large graph clustering.