A self-organizing algorithm for community structure analysis in complex networks

Sun, Hanlin, Jie, Wei ORCID: https://orcid.org/0000-0002-5392-0009, Sauer, Christian, Ma, Sugang, Han, Gang and Xing, Wei (2016) A self-organizing algorithm for community structure analysis in complex networks. In: 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2016), 30 May - 01 Jun 2016, Shanghai, China.

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Community structure analysis is a critical task for complex network analysis. It helps us to understand the properties of the system that a complex network represents, and has significance to a wide range of real applications. The Label Propagation Algorithm (LPA) is currently the most popular community structure analysis algorithm due to its near linear time complexity. However, the performance of the LPA has proven to be unstable and the correctness of community assignment of nodes is unsatisfactory. In this paper a Self-Organizing Community Detection and Analytic Algorithm (SOCDA2) based on swarm intelligence is proposed. In the algorithm, a network is modeled as a swarm intelligence system, while each node within the network acts iteratively to join or leave communities based on a set of pre-defined node action rules, in order to improve the quality of the communities. When there is not a node changing its belonging community anymore, an optimal community structure will emerge as a result. A variety of experiments conducted on both synthesized and real-world networks have shown results which indicate that the proposed algorithm can effectively detect community structures and the performance is better than that of the LPA. In addition, the algorithm can be extended for overlapping community detection and be parallelized for largescale network analysis.

Item Type: Conference or Workshop Item (Paper)
ISBN: 9781509008049
Identifier: 10.1109/SNPD.2016.7515870
Identifier: 10.1109/SNPD.2016.7515870
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Algorithm design and analysis, Signal to noise ratio, Silicon, Complex networks, Genetic algorithms, Particle swarm optimization, Partitioning algorithms
Subjects: Computing
Depositing User: WEI JIE
Date Deposited: 24 Mar 2016 10:56
Last Modified: 28 Aug 2021 07:19
URI: https://repository.uwl.ac.uk/id/eprint/1858


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