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Multi-objective memetic algorithm for core-periphery structure detection in complex network

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Abstract

Core-periphery structure detection (CPSD) in complex networks is essential to reveal functional nodes in the complicated systems, e.g., influential nodes in a social network and central cells in a biological network. Some progress has been made in solving the CPSD problem with heuristic algorithms. However, CPSD is naturally an NP-hard optimization problem and the core-periphery structures (CPSs) in real networks usually are not clearly distinguishable. The majority of the existing CPSD methods are single-objective methods relying on some assumptions, preference, and/or prior knowledge. They can provide only one trade-off solution that is inevitably biased and lacks of flexibility in terms of resolution. To address this issue, this paper formulates the CPSD problem as a multi-objective optimization problem (MOP), i.e., minimizing the core-node size and maximizing the core-node capacity of the CPSs, simultaneously. Solving the MOP can provide more accurate CPSs and allow one to explore the network structure at different preferred resolutions. A multi-objective memetic algorithm (called MOMA-PCLS) is accordingly proposed to solve the formulated problem. A new plateau-climbing local search (PCLS) method incorporating the information of the heavy-tailed distribution of the node capacity is introduced to fine-tune the individual solutions in MOMA-PCLS. By combining the evolutionary operations and PCLS, MOMA-PCLS manages to improve the search efficiency significantly. Experimental results on both synthetic and real-world data show the superiority of MOMA-PCLS to other state-of-the-art algorithms in detecting CPSs of complex networks.

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Funding

This work was supported in part by National Natural Science Foundation of China [61871272 and 61803269], in part by the Guangdong Provincial Key Laboratory [2020B121201001 and 2020A1515010790 ], in part by the Shenzhen Fundamental Research Program [JCYJ20190808173617147 and JCYJ20190808174801673], and in part by the BGI-Research Shenzhen Open Funds [BGIRSZ20200002].

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Guo Li, Zexuan Zhu, Lijia Ma & Xiaoliang Ma

  2. Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, 518055, China

    Zexuan Zhu

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  1. Guo Li
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  4. Xiaoliang Ma
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Correspondence to Zexuan Zhu or Lijia Ma.

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Li, G., Zhu, Z., Ma, L. et al. Multi-objective memetic algorithm for core-periphery structure detection in complex network. Memetic Comp. 13, 285–306 (2021). https://doi.org/10.1007/s12293-021-00342-9

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