Salīdzināt metodes

	Svērtā modulitātes analīze	Svaru kopienu noteikšana
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2004	2004–2008
Autors	Newman, M. E. J.	Newman, M. E. J.; Blondel et al.
Tips	Community structure optimization on weighted graphs	Graph clustering / community detection
Pirmavots	Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131.	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.
Citi nosaukumi	weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD
Saistītās	5	6
Kopsavilkums	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.	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.