Complex network graph embedding method based on shortest path and MOEA/D for community detection

As one of the main applications of graph embedding, community detection has always been a hot issue in the field of complex network data mining. This paper presents a complex network graph embedding method based on the shortest path matrix and decomposition multi-objective evolutionary algorithm (SP-MOEA/D) for community detection, which can better reflect the network structure at the level of network community structure. Firstly, by calculating the shortest path matrix between nodes in the network, the node relationship matrix is obtained by adding the node similarity. Next, aiming at the problem of community detection in disconnected networks, a decomposition-based multi-objective optimization method is proposed to assign distances to unrelated nodes. Then, the network similarity matrix is calculated based on the relationship matrix of network nodes, and the low-dimensional vector representation of nodes is obtained by random surfing strategy and multi-dimensional scaling method. Finally, the community structure of the network can be detected based on the obtained node representation structure. Starting from the essence of network structure and the tightness between nodes, this method can reflect the relationship characteristics of network nodes more effectively, and then obtain the vector representation of nodes which can more accurately reflect the information of community structure in networks. The test results on 11 networks show that the node vector representation results obtained by this method can better reflect the community structure information in complex networks. (C) 2020 Elsevier B.V. All rights reserved.