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Open AccessArticle

A SOM-Based Membrane Optimization Algorithm for Community Detection

School of Information Engineering, Shenyang University, Liaoning 110044, China
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Entropy 2019, 21(5), 533; https://doi.org/10.3390/e21050533
Received: 26 April 2019 / Revised: 17 May 2019 / Accepted: 23 May 2019 / Published: 25 May 2019
(This article belongs to the Special Issue Computation in Complex Networks)
The real world is full of rich and valuable complex networks. Community structure is an important feature in complex networks, which makes possible the discovery of some structure or hidden related information for an in-depth study of complex network structures and functional characteristics. Aimed at community detection in complex networks, this paper proposed a membrane algorithm based on a self-organizing map (SOM) network. Firstly, community detection was transformed as discrete optimization problems by selecting the optimization function. Secondly, three elements of the membrane algorithm, objects, reaction rules, and membrane structure were designed to analyze the properties and characteristics of the community structure. Thirdly, a SOM was employed to determine the number of membranes by learning and mining the structure of the current objects in the decision space, which is beneficial to guiding the local and global search of the proposed algorithm by constructing the neighborhood relationship. Finally, the simulation experiment was carried out on both synthetic benchmark networks and four real-world networks. The experiment proved that the proposed algorithm had higher accuracy, stability, and execution efficiency, compared with the results of other experimental algorithms. View Full-Text
Keywords: community detection; membrane algorithm; self-organizing map network; complex networks; optimization community detection; membrane algorithm; self-organizing map network; complex networks; optimization
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Liu, C.; Du, Y.; Lei, J. A SOM-Based Membrane Optimization Algorithm for Community Detection. Entropy 2019, 21, 533.

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