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A hybrid evolutionary algorithm for community detection

Published: 23 August 2017 Publication History

Abstract

Evolutionary algorithm belongs to the behaviorism which is one of major approaches to artificial intelligence. Community detection is one of the important applications of the evolutionary algorithm. Detecting the community structure, an essential property for complex networks, can help us understand the inherent functions of real systems. It has been proved that genetic algorithm (GA) is feasible for community detection, and yet existing GA-based community detection algorithms still need improving in terms of their robustness and accuracy. A Physarum-based network model (PNM) with an intelligence of recognizing inter-community edges based on a kind of multi-headed slime mold, has been proposed in the phase of GA's initialization for optimization. In this paper, integrated with PNM after three operators of GA during the process of community detection, a novel genetic algorithm, called P-GACD, is proposed to improve the efficiency of GA for community detection. In addition, some experiments are implemented in five real-world networks to evaluate the performance of P-GACD. The results reveal that P-GACD shows an advantage in terms of the robustness and accuracy, contrasted with the existing algorithms.

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  • (2020)Detecting Communities in Networks: a Decentralized Approach Based on Multiagent Reinforcement Learning2020 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI47803.2020.9308197(2225-2232)Online publication date: 1-Dec-2020
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  • (2019)Evolutionary Community Detection in Dynamic Social Networks2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852006(1-7)Online publication date: Jul-2019
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    cover image ACM Conferences
    WI '17: Proceedings of the International Conference on Web Intelligence
    August 2017
    1284 pages
    ISBN:9781450349512
    DOI:10.1145/3106426
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    Published: 23 August 2017

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    Author Tags

    1. physarum
    2. community detection
    3. complex networks
    4. genetic algorithm

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    WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;
    Overall Acceptance Rate 118 of 178 submissions, 66%

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    View all
    • (2020)Detecting Communities in Networks: a Decentralized Approach Based on Multiagent Reinforcement Learning2020 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI47803.2020.9308197(2225-2232)Online publication date: 1-Dec-2020
    • (2019)Hierarchical Multi-dimensional Attention Model for Answer Selection2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852055(1-8)Online publication date: Jul-2019
    • (2019)Evolutionary Community Detection in Dynamic Social Networks2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852006(1-7)Online publication date: Jul-2019
    • (2019)Learning Distributed Coordinated Policy in Catching Game with Multi-Agent Reinforcement Learning2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851905(1-7)Online publication date: Jul-2019
    • (2019)Detecting the evolving community structure in dynamic social networksWorld Wide Web10.1007/s11280-019-00710-zOnline publication date: 23-Oct-2019

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