Evolutionary Markov Dynamics for Network Community Detection

• Published in: IEEE transactions on knowledge and data engineering Vol. 34; no. 3; pp. 1206 - 1220
• Main Authors: Wang, Zhen, Wang, Chunyu, Li, Xianghua, Gao, Chao, Li, Xuelong, Zhu, Junyou
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Amoeba; Apexes; Clustering algorithms; community detection; Complex networks; Computational efficiency; Computing costs; Data analysis; Detection algorithms; dynamic networks; Flow distribution; genetic algorithm; Genetic algorithms; Heuristic algorithms; Inhomogeneity; Iterative methods; Markov chains; Markov clustering algorithm; Markov processes; Mathematical model; Measurement; physarum; Positive feedback; Transition probabilities
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2020.2997043

Community structure division is a crucial problem in the field of network data analysis. Algorithms based on Markov chains are easy to use and provide promising solutions for community detection. In a Markov chain-based algorithm (i.e., MCL), a flow distribution matrix and a transition matrix are used to describe stochastic flows and transition probabilities, respectively, on a network. The dynamic interaction process between stochastic flows and transition probabilities in MCLs is manifested through an iterative process of updating the abovementioned two matrices. As one of the key mechanisms of MCLs, such a dynamic process for increasing the inhomogeneity directly affects the accuracy and computational cost of MCL-based methods. Inspired by a kind of positive feedback interaction of a dendritic network of tube-like amoeba cell pseudopodia (named the Physarum foraging network), a Physarum -inspired relationship among vertices is proposed to enhance the transition probability in the dynamic process of MCL-based community detection algorithms. Specifically, the proposed hybrid community detection algorithm can adaptively search for a better combination of parameters based on a genetic algorithm. Some experiments are carried out on both static and dynamic networks. The results show that the unique Physarum inspired algorithm achieved better computational efficiency and detection performance than other algorithms.