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Self-similarity of complex networks under centrality-based node removal strategy |
| Dan Chen(陈单)1,2, Defu Cai(蔡德福)3, and Housheng Su(苏厚胜)1,2,† |
1 School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;
2 Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074, China;
3 State Grid Hubei Electric Power Research Institute, Wuhan 430077, China |
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Abstract Real-world networks exhibit complex topological interactions that pose a significant computational challenge to analyses of such networks. Due to limited resources, there is an urgent need to develop dimensionality reduction techniques that can significantly reduce the structural complexity of initial large-scale networks. In this paper, we propose a subgraph extraction method based on the node centrality measure to reduce the size of the initial network topology. Specifically, nodes with smaller centrality value are removed from the initial network to obtain a subgraph with a smaller size. Our results demonstrate that various real-world networks, including power grids, technology, transportation, biology, social, and language networks, exhibit self-similarity behavior during the reduction process. The present results reveal the self-similarity and scale invariance of real-world networks from a different perspective and also provide an effective guide for simplifying the topology of large-scale networks.
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Received: 24 March 2023
Revised: 26 April 2023
Accepted manuscript online: 10 May 2023
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PACS:
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89.75.-k
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(Complex systems)
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89.75.Fb
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(Structures and organization in complex systems)
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89.75.Hc
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(Networks and genealogical trees)
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| Fund: Project supported by the Science and Technology Project of State Grid Corporation of China (Grant No. 5100- 202199557A-0-5-ZN). |
Corresponding Authors:
Housheng Su
E-mail: houshengsu@gmail.com
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Cite this article:
Dan Chen(陈单), Defu Cai(蔡德福), and Housheng Su(苏厚胜) Self-similarity of complex networks under centrality-based node removal strategy 2023 Chin. Phys. B 32 098903
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