An autoencoder considering multi-order and structural-role similarity for community detection in attributed networks

A community is composed of closely related nodes. Detecting communities in a network has many practical applications, such as online product recommendation, biological molecule discovery and criminal group tracking. In recent years, network representation learning (NRL) has attracted much attention in the field of community detection because it can effectively extract complex relations between nodes which improves the quality of detected communities. In many real-world networks, the rich attribute information contained in nodes and the similarity between nodes and their multi-order neighbors has significant contributions to the generation of node embedding vectors in NRL. However, existing NRL algorithms treat a node’s different order neighbors equally as high-order contexts, leading to the ignorance of their different impacts on the generation of the node’s embedding vector. In addition, these algorithms do not focus on the coupling and interaction relations between nodes playing similar structural roles, which may ignore some nodes in a community with similar structural roles. In this paper, we propose a novel autoencoder considering the multi-order similarity and structural role similarity (AMOSOS) to solve the above problems. First, we design a strategy to obtain a multi-order weight matrix which preserves the differential influence of neighbors of different orders by sequentially decreasing the weight of each order. Second, we design a role similarity indicator to capture the complex coupling and interaction relations of nodes in the network. Experimental results on synthetic networks and real-world networks show that our proposed algorithm is more accurate than existing network representation learning algorithms for the task of community detection.