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A novel robust memetic algorithm for dynamic community structures detection in complex networks

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Abstract

Networks in the real world are dynamic and evolving. The most critical process in networks is to determine the structure of the community, based on which we can detect hidden communities in a complex network. The design of strong network structures is of great importance, meaning that a system must maintain its function in the face of attacks and failures and have a strong community structure. In this paper, we proposed the robust memetic algorithm and used the idea to optimize the detection of dynamic communities in complex networks called RDMA_NET (Robust Dynamic Memetic Algorithm). In this method, we work on dynamic data that affects the two main parts of the initial population value and the calculation of the evaluation function of each population, and there is no need to determine the number of communities in advance. We used two sets of real-world networks and the LFR dataset. The results show that our proposed method, RDMA_Net, can find a better solution than modern approaches and provide near-optimal performance in search of network topologies with a strong community structure.

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Data availability

The datasets used in this paper are publicly available in the following links.

http://snap.stanford.edu/data.

https://west.uni-koblenz.de/konect.

https://networkrepository.com.

http://staff.icar.cnr.it/pizzuti/codes.html.

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Somayeh Ranjkesh & Seyyed Mohsen Hashemi

  2. Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Behrooz Masoumi

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  1. Somayeh Ranjkesh
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  3. Seyyed Mohsen Hashemi
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All authors contributed equally in writing this paper. All authors reviewed the manuscript.

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Correspondence to Behrooz Masoumi.

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Ranjkesh, S., Masoumi, B. & Hashemi, S.M. A novel robust memetic algorithm for dynamic community structures detection in complex networks. World Wide Web 27, 3 (2024). https://doi.org/10.1007/s11280-024-01238-7

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