SMACD: Semi-supervised multi-aspect community detection

Abstract

Community detection in real-world graphs has been shown to benefit from using multi-aspect information, e.g., in the form of "means of communication" between nodes in the network. An orthogonal line of work, broadly construed as semi-supervised learning, approaches the problem by introducing a small percentage of node assignments to communities and propagates that knowledge throughout the graph. In this paper we introduce SMACD, a novel semi-supervised multi-aspect community detection method along with an automated parameter tuning algorithm which essentially renders SMACD parameter-free. To the best of our knowledge, SMACD is the first approach to incorporate multi-aspect graph information and semi-supervision, while being able to discover overlapping and non-overlapping communities. We extensively evaluate SMACD's performance in comparison to state-of-the-art approaches across eight real and two synthetic datasets, and demonstrate that SMACD, through combining semi-supervision and multi-aspect edge information, outperforms the baselines.