Comparer des méthodes

	Analyse pondérée de graphes de connaissances	Centralité de proximité pondérée
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2010s–present	2010
Auteur d'origine	Hogan et al. and the broader knowledge graph community	Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001)
Type	Network analysis variant	Centrality measure (path-based)
Source fondatrice	Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1–37.	Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.
Alias	WKGA, weighted KG analysis, confidence-weighted knowledge graph, weighted semantic network analysis	WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted)
Apparentées	6	6
Résumé	Weighted Knowledge Graph Analysis extends standard knowledge graph methods by assigning numerical weights — such as confidence scores, co-occurrence frequencies, or relation strengths — to edges between entities. These weights allow analysts to prioritise high-confidence triples, find the most influential paths, and compute weight-aware centrality and community structure in large structured knowledge bases.	Weighted Betweenness Centrality extends Freeman's betweenness measure to edge-weighted graphs by routing shortest paths through a tunable transformation of edge weights. Nodes that sit on many high-value shortest paths receive high scores, identifying brokers and bridges in social, biological, and information networks where tie strength matters.