Machine learningNetwork science
加权社区检测
加权社区检测识别网络中密集连接的组（社区），其中边具有数值强度（权重）。通过将边权重纳入模块度函数，它可以揭示仅靠二元邻接会遗漏的结构：两个节点之间具有强连接的节点比仅具有弱连接的节点更相似。Louvain算法是主要的实际实现方法。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
+2 more
来源
- Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. DOI: 10.1088/1742-5468/2008/10/P10008 ↗
- Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131. DOI: 10.1103/PhysRevE.70.056131 ↗
如何引用本页
ScholarGate. (2026, June 3). Weighted Community Detection in Networks. ScholarGate. https://scholargate.app/zh/network-analysis/weighted-community-detection
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 社群检测网络分析↔ compare
- 模块度分析网络分析↔ compare
- 多层网络分析网络分析↔ compare
- 社会网络分析网络分析↔ compare
- 加权模块度分析网络分析↔ compare
- 加权社会网络分析网络分析↔ compare