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#6029. Multi-objective memetic algorithm for core-periphery structure detection in complex network
| July 2026 | publication date |
| Proposal available till | 28-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Control and Optimization; Computer Science (all); |
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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.
Keywords:
Complex networks; Core-periphery structure; Memetic algorithm; Multi-objective optimization
Contacts :
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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.
Keywords:
Complex networks; Core-periphery structure; Memetic algorithm; Multi-objective optimization
Contacts :
