TY - JOUR
T1 - Network Tight Community Detection
AU - Deng, Jiayi
AU - Yang, Xiaodong
AU - Yu, Jun
AU - Liu, Jun S.
AU - Shen, Zhaiming
AU - Huang, Danyang
AU - Cheng, Huimin
N1 - Publisher Copyright:
Copyright 2024 by the author(s)
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85203821909&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85203821909
SN - 2640-3498
VL - 235
SP - 10574
EP - 10596
JO - Proceedings of Machine Learning Research
JF - Proceedings of Machine Learning Research
T2 - 41st International Conference on Machine Learning, ICML 2024
Y2 - 21 July 2024 through 27 July 2024
ER -