Graph Neural Network Encoding for Community Detection in Attribute Networks

In this article, we first propose a graph neural network encoding method for the multiobjective evolutionary algorithm (MOEA) to handle the community detection problem in complex attribute networks. In the graph neural network encoding method, each edge in an attribute network is associated with a continuous variable. Through nonlinear transformation, a continuous valued vector (i.e., a concatenation of the continuous variables associated with the edges) is transferred to a discrete valued community grouping solution. Further, two objective functions for the single-attribute and multiattribute network are proposed to evaluate the attribute homogeneity of the nodes in communities, respectively. Based on the new encoding method and the two objectives, a MOEA based upon NSGA-II, called continuous encoding MOEA, is developed for the transformed community detection problem with continuous decision variables. Experimental results on single-attribute and multiattribute networks with different types show that the developed algorithm performs significantly better than some well-known evolutionary- and nonevolutionary-based algorithms. The fitness landscape analysis verifies that the transformed community detection problems have smoother landscapes than those of the original problems, which justifies the effectiveness of the proposed graph neural network encoding method.