Network Tight Community Detection

Jiayi Deng, Xiaodong Yang, Jun Yu, Jun S. Liu, Zhaiming Shen, Danyang Huang, Huimin Cheng*

*此作品的通讯作者

科研成果: 期刊稿件会议文章同行评审

摘要

Conventional community detection methods often categorize all nodes into clusters. However, the presumed community structure of interest may only be valid for a subset of nodes (named as “tight nodes”), while the rest of the network may consist of noninformative “scattered nodes”. For example, a protein-protein network often contains proteins that do not belong to specific biological functional modules but are involved in more general processes, or act as bridges between different functional modules. Forcing each of these proteins into a single cluster introduces unwanted biases and obscures the underlying biological implication. To address this issue, we propose a tight community detection (TCD) method to identify tight communities excluding scattered nodes. The algorithm enjoys a strong theoretical guarantee of tight node identification accuracy and is scalable for large networks. The superiority of the proposed method is demonstrated by various synthetic and real experiments.

源语言英语
页(从-至)10574-10596
页数23
期刊Proceedings of Machine Learning Research
235
出版状态已出版 - 2024
活动41st International Conference on Machine Learning, ICML 2024 - Vienna, 奥地利
期限: 21 7月 202427 7月 2024

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