復雜網(wǎng)絡的社團結構分析(存儲版)
【正文】 k 28 致謝 This work is cooperated with Dr. 李珍萍， Dr. 王瑞省， Dr. 王勇， Dr. 張世華， Dr. 王吉光， Dr. 張俊華 This work is supported by 國家自然科學重點基金 10631070 973項目 2066CB503905 國家自然科學基金項目 60873205 29 30 ? 歡迎訪問 ZHANGroup, 本報告可在該網(wǎng)頁上下載 。 Sune Lehmann， Nature, 2022 生物信息學與最優(yōu)化方法 7 Importance of the topic 社團結構探索方法概述 A large number of methods have been developed for detecting munities, which can be generally categorized into local and global methods. ? Local methods for munity detection identify a subset of nodes as a munity according to certain local connection conditions, independently from the structure of the rest of the work. Such methods include clique overlapbased hierarchical clustering, clique percolation method, and subgraph fitness method. ? Global methods for munity detection optimize certain global quantitative functions encoding the quality of the overall partition of the work, such as information theoretical method, Potts model, and optimization of modularity measures. 8 9 Shihua Zhang, RuiSheng Wang, and XiangSun Zhang. Identification of overlapping munity structure in plex works using fuzzy cmeans Clustering. Physica A, 2022, 374, 483–490. RuiSheng Wang, Shihua Zhang, Yong Wang, XiangSun Zhang, Luonan Chen. Clustering plex works and biological works by nonnegativ
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