Comparer des méthodes

	Centralité de Proximité Pondérée	Analyse des réseaux sociaux pondérés
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2010	2004–2010
Auteur d'origine	Opsahl, T.; Agneessens, F.; Skvoretz, J.	Barrat, A.; Opsahl, T. et al.
Type	Centrality measure (network analysis)	Network analysis framework
Source fondatrice	Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.	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.
Alias	weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness	Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis
Apparentées	6	6
Résumé	Weighted closeness centrality extends the classic closeness measure to networks where edges carry numerical weights — such as frequency, strength, or cost — by incorporating those weights into shortest-path distances. Nodes that can reach others quickly along strong or efficient connections receive higher scores, making it a richer indicator of information-spreading potential than its binary counterpart.	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.