Network Tight Community Detection
Jiayi Deng, Xiaodong Yang, Jun Yu, Jun Liu, Zhaiming Shen, Danyang Huang, Huimin Cheng
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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper1
问问它们各自怎么用它相关 Paper
- Provable Overlapping Community Detection in Weighted GraphsJimit Majmudar, Stephen A. VavasisNeurIPS 2020 · 被引用 5 次
- Weighted Flow Diffusion for Local Graph Clustering with Node Attributes: an Algorithm and Statistical GuaranteesShenghao Yang, Kimon FountoulakisICML 2023 · 被引用 6 次
- Semi-supervised Community Detection via Structural Similarity MetricsYicong Jiang, Tracy KeICLR 2023
- An Adaptive Sampling Algorithm for the Top- Group Betweenness CentralityWenzheng Xu, Honglin Mao, Heng Shao, Weifa Liang 等ICDE 2025 · 被引用 3 次
- Reliable Community Search on Uncertain GraphsXiaoye Miao, Yue Liu, Lu Chen, Yunjun Gao 等ICDE 2022 · 被引用 18 次
