Machine learningNetwork science

Weighted Community Detection

Weighted community detection identifies densely connected groups — communities — in networks where edges carry numeric strengths (weights). By incorporating edge weights into the modularity function, it reveals structure that binary adjacency alone would miss: two nodes connected by a strong tie are treated as more similar than two nodes linked by a weak one. The Louvain algorithm is the dominant practical implementation.

Open in MethodMindSoonVideoSoon

Read the full method

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. 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
  2. Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131. DOI: 10.1103/PhysRevE.70.056131

Related methods

Referenced by

ScholarGateWeighted Community Detection (Weighted Community Detection in Networks). Retrieved 2026-06-04 from https://scholargate.app/en/network-analysis/weighted-community-detection