Salīdzināt metodes

	Svaru kopienu noteikšana	Svērta sociālā tīkla analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2004–2008	2004–2010
Autors	Newman, M. E. J.; Blondel et al.	Barrat, A.; Opsahl, T. et al.
Tips	Graph clustering / community detection	Network analysis framework
Pirmavots	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.	Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.
Citi nosaukumi	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD	Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis
Saistītās	6	6
Kopsavilkums	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.	Weighted Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships.