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复杂系统与复杂性科学  2024, Vol. 21 Issue (2): 15-21    DOI: 10.13306/j.1672-3813.2024.02.002
  复杂网络 本期目录 | 过刊浏览 | 高级检索 |
复杂网络深度重叠结构的发现
高峰
三峡大学理学院,湖北 宜昌 443002
Discovery of Deep Overlapping Structures in Complex Networks
GAO Feng
College of Science, China Three Gorges University, Yichang 443002,China
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摘要 为了更好地了解网络,以相似度为基础,让节点选择多个相似节点形成相似节点对,通过蒙卡模拟结果提出了基于最大节点相似度和度的配对算法发现了网络的重叠社团结构。利用多级最相似度继续优化社团结构,找出了网络社团的深层重叠结构和子社团结构。提出的算法从真实网络形成社团的原因出发发现了网络的重叠结构,并且进一步优化社团结构,发现了网络的深层重叠社团结构和其中的子社团结构。
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高峰
关键词 复杂网络社团结构深度重叠结构子社团结构    
Abstract:In order to better understand the network, based on the similarity, let the nodes select multiple similar nodes to form similar node pairs. Through the Monte Carlo simulation results, a pairing algorithm based on the maximum node similarity and degree is proposed to discover the overlapping community structure of the network. Using multi-level most similarity to continue to optimize the community structure, find out the deep overlapping structure and sub-community structure of the network community. The proposed algorithm discovers the overlapping structure of the network based on the reason why the real network forms a community, and further optimizes the community structure, discovering the deep overlapping community structure of the network and its sub-community structure.
Key wordscomplex network    community structure    deep overlapping structure    sub-community structure
收稿日期: 2022-11-10      出版日期: 2024-07-17
ZTFLH:  TP391  
  O157  
基金资助:国家自然科学基金(11547003)
作者简介: 第一作者: 高峰(1997-),男,湖北咸宁人,硕士研究生,主要研究方向为复杂网络。
引用本文:   
高峰. 复杂网络深度重叠结构的发现[J]. 复杂系统与复杂性科学, 2024, 21(2): 15-21.
GAO Feng. Discovery of Deep Overlapping Structures in Complex Networks[J]. Complex Systems and Complexity Science, 2024, 21(2): 15-21.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2024.02.002      或      https://fzkx.qdu.edu.cn/CN/Y2024/V21/I2/15
[1]PAWAN K, RAVINS D. Formalising and detecting community structures in real world complex networks[J].Journal of Systems Science & Complexity,2021,34(1):180-205.
[2]李辉,陈福才,张建朋,等. 复杂网络中的社团发现算法综述[J]. 计算机应用研究,2021,38(6):1611-1618.
LI H,CHEN F,ZHANG J, et al. Survey of community detection algorithms in complex network[J]. Application Research of Computers,2021,38(6):1611-1618.
[3]CHENG F, WANG C, ZHANG X, et al. A local-neighborhood information based overlapping community detection algorithm for large-scale complex networks[J]. IEEE/ACM Transactions on Networking, 2021,29(2): 543-556.
[4]CHAKRABORTY S, MUHURI S, Das D. Detection of constant member and overlapping community from dynamic literary network[J]. Social Network Analysis and Mining,2021,11(1):77.
[5]李永宁, 吴晔, 张伦. 动态社团发现研究综述[J]. 复杂系统与复杂性科学, 2021, 18(2): 1-8.
LI Y, WU Y, ZHANG L. A review of dynamic community detection[J]. Complex Systems and Complexity Science, 2021, 18(2): 1-8.
[6]NEWMAN M, GIRVAN M. Finding and evaluating community structure in networks[J].Physical Review E, 2004, 69(2):026113.
[7]KATHY M, TOLGA C, AMBUJ K. RRW: repeated random walks on genome-scale protein networks for local cluster discovery[J]. BMC Bioinformatics, 2009, 10(1): 283.
[8]LI J, WANG X, CUI Y. Uncovering the overlapping community structure of complex networks by maximal cliques[J]. Physica A: Statistical Mechanics and Its Applications, 2014, 415(1): 398-406.
[9]HAJIABADI M,ZARE H, Bobarshad H. IEDC: An integrated approach for overlapping and non-overlapping community detection[J]. Knowledge-Based Systems, 2017, 123(1):188-199.
[10] NICOSIA V, MANGIONI G, CARCHIOLO V, et al. Extending the definition of modularity to directed graphs with overlapping communities[J]. Journal of Statistical Mechanics Theory & Experiment, 2009, 2009(3):3166-3168.
[11] LIU H, LI G. Overlapping community detection method based on network representation learning and density peaks[J].IEEE Access, 2020, 8(99):1.
[12] SHI P, HE K, BINDEL D, et al. Locally-biased spectral approximation for community detection[J]. Knowledge-Based Systems, 2019, 164(1):459-472.
[13] DEVI J C, POOVAMMAL E. An analysis of overlapping community detection algorithms in social networks[J]. Procedia Computer Science, 2016, 89:349-358.
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