A Framework for Accurate Community Detection on Signed Networks Using Adversarial Learning

In this article, we propose a framework for embedding-based community detection on signed networks, namely A dversarial learning of B alanced triangle for C ommunity detection, in short ${{\sf ABC}}$ . It first represents all the nodes of a signed network as vectors in low-dimensional embedding space and conducts a clustering algorithm (e.g., k -means) on vectors, thereby detecting a community structure in the network. When performing the embedding process, ${{\sf ABC}}$ learns only the edges belonging to balanced triangles whose edge signs follow the balance theory, significantly excluding noise edges in learning. To address the sparsity of balanced triangles in a signed network, ${{\sf ABC}}$ learns not only the edges in balanced real -triangles but those in balanced virtual -triangles that do not actually exist but are produced by our generator. Finally, ${{\sf ABC}}$ employs adversarial learning to generate more-realistic balanced virtual-triangles with less noise edges. Through extensive experiments using seven real-world networks, we validate the effectiveness of (1) learning edges belonging to balanced real/virtual-triangles and (2) employing adversarial learning for signed network embedding. We show that ${{\sf ABC}}$ consistently and significantly outperforms the state-of-the-art community detection methods in all datasets.