A Parallel multi-objective evolutionary algorithm for community detection in large-scale complex networks

2021 ◽  
Author(s):  
Yansen Su ◽  
Kefei Zhou ◽  
Xingyi Zhang ◽  
Ran Cheng ◽  
Chunhou Zheng
Keyword(s):  
Complex Networks ◽  
Evolutionary Algorithm ◽  
Community Detection ◽  
Large Scale ◽  
Multi Objective

A Network Reduction-Based Multiobjective Evolutionary Algorithm for Community Detection in Large-Scale Complex Networks

2020 ◽  
Vol 50 (2) ◽  
pp. 703-716 ◽  
Author(s):  
Xingyi Zhang ◽  
Kefei Zhou ◽  
Hebin Pan ◽  
Lei Zhang ◽  
Xiangxiang Zeng ◽  
...  
Keyword(s):  
Complex Networks ◽  
Evolutionary Algorithm ◽  
Community Detection ◽  
Large Scale ◽  
Network Reduction ◽  
Multiobjective Evolutionary Algorithm

A local information based multi-objective evolutionary algorithm for community detection in complex networks

2018 ◽  
Vol 69 ◽  
pp. 357-367 ◽  
Author(s):  
Fan Cheng ◽  
Tingting Cui ◽  
Yansen Su ◽  
Yunyun Niu ◽  
Xingyi Zhang
Keyword(s):  
Complex Networks ◽  
Evolutionary Algorithm ◽  
Community Detection ◽  
Local Information ◽  
Multi Objective

A multi-objective ant colony optimization with decomposition for community detection in complex networks

2019 ◽  
Vol 41 (9) ◽  
pp. 2521-2534 ◽  
Author(s):  
Ruochen Liu ◽  
Jiangdi Liu ◽  
Manman He
Keyword(s):  
Complex Networks ◽  
Ant Colony Optimization ◽  
Community Detection ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Network Clustering ◽  
Multi Objective ◽  
Search Mechanism ◽  
Large Scale Networks

Community detection in complex networks plays an important role in mining and analyzing the structure and function of networks. However, traditional algorithms for community detection-based graph partition and hierarchical clustering usually have to face expensive computational costs or require some specific conditions when dealing with complex networks. Recently, community detection based on intelligent optimization attracts more and more attention because of its good effectiveness. In this paper, a new multi-objective ant colony optimization with decomposition (MACOD) for community detection in complex networks is proposed. Firstly, a new framework of multi-objective ant colony algorithm specialized initially for the complex network clustering is developed, in which two-objective optimization problem can be decomposed into a series of subproblems and each ant is responsible for one single objective subproblem and it targets a particular point in the Pareto front. Secondly, a problem-specific individual encoding strategy based on graph is proposed. Moreover, a new efficient local search mechanism is designed in order to improve the stability of the algorithm. The proposed MACOD has been compared with four other state of the art algorithms on two benchmark networks and seven real-world networks including three large-scale networks. Experimental results show that MACOD performs competitively for the community detection problems.

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