Machine learningNetwork science

Weighted Modularity Analysis

Weighted modularity analysis extends the classical Newman-Girvan modularity measure to networks where edges carry numeric strengths (frequencies, intensities, costs). By replacing binary adjacency with tie weights, it finds community partitions that reflect how densely interconnected subgroups are relative to what is expected under a weighted null model, yielding more nuanced groupings than unweighted approaches on data where edge strength varies meaningfully.

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Sources

  1. Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131. DOI: 10.1103/PhysRevE.70.056131
  2. Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582. DOI: 10.1073/pnas.0601602103

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Referenced by

ScholarGateWeighted Modularity Analysis (Weighted Modularity Analysis (Q-weighted community structure detection)). Retrieved 2026-06-04 from https://scholargate.app/en/network-analysis/weighted-modularity-analysis