LAUSR

Abstract An attributed graph is a graph where nodes are associated with attributes describing their features. Clustering on an attributed graph is to detect clusters that have not only (1) cohesive structure; but also (2) homogeneous attribute values. We observe that in an attributed graph, different clusters tend to correlate to different attributes. For example, a cluster may show homogeneous values on attribute A but random values on attribute B, while for another cluster the situation could be the reverse. Existing attributed graph clustering methods ignore this phenomenon, thus leading to unsatisfactory clustering results. In this paper, we propose a novel attributed graph clustering framework called C o H omo to detect clusters with different attribute correlation patterns. The objective is to detect clusters that show both high structural cohesiveness and value homogeneity on some of the attributes. C o H omo defines and optimizes a correlation weight vector for each cluster to capture its correlation degrees to different node attributes. We formalize this problem as a bi-objective optimization problem and design an efficient heuristic optimizing method. The correlation weights for a cluster can be updated in two ways, synchronous updating or asynchronous updating, during the process of clustering. C o H omo is parameter-free and has near linear scalability. Theoretical analysis shows that all the results on the Pareto front generated by C o H omo are weak Pareto optimal solutions. Extensive experiments on attributed graphs based on several real world networks verify the effectiveness and show that our method outperforms the state-of-the-art method.