SpEC: Sparse Embedding-Based Community Detection in Attributed Graphs

Abstract

Community detection, also known as graph clustering, is a widely studied task to find the subgraphs (communities) of related nodes in a graph. Existing methods based on non-negative matrix factorization can solve the task of both non-overlapping community detection and overlapping community detection, but the probability vector obtained by factorization is too dense and ambiguous, and the difference between these probabilities is too small to judge which community the corresponding node belongs to. This will lead to a lack of interpretability and poor performance in community detection. Besides, there are always many sparse subgraphs in a graph, which will cause unstable iterations. Accordingly, we propose SpEC (Sparse Embedding-based Community detection) for solving the above problems. First, sparse embeddings has stronger interpretability than dense ones. Second, sparse embeddings consume less space. Third, sparse embeddings can be computed more efficiently. For traditional matrix factorization-based models, their iteration update rules do not guarantee the convergence for sparse embeddings. SpEC elaborately designs the update rules to ensure convergence and efficiency for sparse embeddings. Crucially, SpEC takes full advantage of attributed graphs and learns the neighborhood patterns, which imply inherent relationships between node attributes and topological structure information. By coupled recurrent neural networks, SpEC recovers the missing edges and predicts the relationship between pairs of nodes. In addition, SpEC ensures stable convergence and improving performance. Furthermore, the results of the experiments show that our model outperforms other state-of-the-art community detection methods.