Self-similarity of complex networks under centrality-based node removal strategy  

作　　者：陈单 蔡德福 苏厚胜 Dan Chen;Defu Cai;Housheng Su(School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China;Institute of Artificial Intelligence,Huazhong University of Science and Technology,Wuhan 430074,China;State Grid Hubei Electric Power Research Institute,Wuhan 430077,China)

机构地区：[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

出　　处：《Chinese Physics B》2023年第9期596-602,共7页中国物理B（英文版）

基　　金：the Science and Technology Project of State Grid Corporation of China(Grant No.5100-202199557A-0-5-ZN)。

摘　　要：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 selfsimilarity 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.

关 键 词：complex networks subgraph extraction SELF-SIMILARITY scale invariance 

分 类 号：O157.5[理学—数学]