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

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....

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Bibliographic Details
Published in:Memetic computing Vol. 13; no. 3; pp. 285 - 306
Main Authors: Li, Guo, Zhu, Zexuan, Ma, Lijia, Ma, Xiaoliang
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2021
Springer Nature B.V
Subjects:
Algorithms
Applications of Mathematics
Artificial Intelligence
Bioinformatics
Complex Systems
Control
Engineering
Heuristic methods
Mathematical and Computational Engineering
Mechatronics
Multiple objective analysis
Nodes
Optimization
Regular Research Paper
Robotics
Social networks
Core-periphery structure
Memetic algorithm
Multi-objective optimization
Complex networks
ISSN:1865-9284, 1865-9292
Online Access:Get full text
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Summary: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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ISSN:1865-9284
1865-9292
DOI:10.1007/s12293-021-00342-9