Link-Based Attributed Graph Clustering via Approximate Generative Bayesian Learning

Abstract

To understand the mechanisms of complex systems, attributed graphs (AGs) are recognized as a valuable model by their capability of describing nontrivial topological structures and rich node contents, and their emergence raises new challenges on the task of graph clustering. Although a variety of computational algorithms have been proposed to perform accurate clustering analysis on AGs, most of them are incapable of inferring the cluster labels of nodes through links, thus falling short of explaining node behaviors on how to formulate overlapping clusters. Moreover, the vast amount of links considerably decreases the computation efficiency if they are explicitly taken into account for AG clustering. To overcome this problem, we present a novel variational Bayesian learning model, which avoids generating a complete AG by only simulating the generative process of its skeleton with the prior knowledge on the cluster labels of links. When addressing the inference problem, we develop an efficient algorithm, namely, LCAAG, for determining the optimal cluster labels of nodes by estimating local community structures of links. The convergence of LCAAG has been proved theoretically. Compared with several state-of-the-art algorithms, LCAAG has demonstrated its promising performance in terms of both accuracy and scalability on five different scaled benchmark datasets. The source code and datasets are available at https://github.com/shallowdreamoon/LCAAG.git.