Continuous Encoding for Community Detection in Attribute Networks with Preserving Node Information

Community detection in complex attribute network is an indispensable but difficult task in data mining. Recently, using multiobjective evolutionary algorithm (MOEA) to address this task has become popular since it can be naturally modeled as a discrete multiobjective optimization problem (MOP). In this paper, we develop a continuous MOEA, in which a continuous encoding is proposed to convert the discrete MOP into a continuous one by introducing a set of auxiliary continuous variables. Further, we construct a similarity matrix to replace the adjacency matrix by making use of the network node degree information in the encoding. The new similarity matrix not only reserves the property of the adjacency matrix but includes the degree information of all the network nodes. In our experiments, various benchmark networks with or without ground truths are used to compare with some state-of-the-art MOEA-based and non-MOEA-based methods. The experimental results show that the proposed algorithm performs favorably against the compared methods.