Quadratic Optimization based Clique Expansion for overlapping community detection

Abstract

Community detection is a crucial task in the field of network analysis. A community is a collection of tightly connected nodes only have sporadic external connections. In many real-world networks, communities naturally overlap, and prior knowledge about them is usually unavailable, such as the number of ground-truth communities in the network. In this work, we present the QOCE (Quadratic Optimization based Clique Expansion), an overlapping community detection method that does not require any prior knowledge. QOCE follows the popular seed set expansion strategy and regards each high-quality maximal clique as the initial seed set. For seed set expansion, QOCE uses a fast short random walk to sample a subgraph from a clique seed set, then adopts a quadratic optimization to approximate the Cheeger cut on the sampled subgraph. Finally, a local minimum of conductance determines the boundary of the community. We extensively evaluate our method by comparing it with four state-of-the-art baseline algorithms on synthetic and real-world networks in various domains and scales. Empirical results demonstrate the competitive performance of our method in terms of detection accuracy and efficiency.