Detect Overlapping Community via Graph Neural Network and Topological Potential

Overlapping community structure is an important characteristic of real complex networks, the goal of the overlapping community detection is to resolve the modular with the information contained in the networks. However, most existing methods based on deep learning techniques directly utilize the original network topology or node attributes, ignoring the importance of various edge information. Inspired by the effective representation learning capability of graph neural network and the ability of topological potential to measure the intimacy between nodes, we propose a novel model, named DOCGT, for overlapping community detection. This model deconstructs the original graph into a first-order graph and a second-order graph, and builds a set of graph neural network modules based on the Bernoulli-Poisson (BP) model, and then uses its advantages to independently learn the node embedding representation of different orders. To this end, we introduce the concept of topological potential matrix. It can not only effectively merge the above embeddings, but also integrate abundant edge information into the entire model. This fused embedding matrix can help us get the final community structure. Experimental results on real datasets show that our method can effectively detect overlapping community structures.