Compara mètodes

	Anàlisi de grafs de coneixement ponderats	Centralitat de Proximitat Ponderada
Camp	Anàlisi de xarxes	Anàlisi de xarxes
Família	Machine learning	Machine learning
Any d'origen	2010s–present	2010
Autor original	Hogan et al. and the broader knowledge graph community	Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001)
Tipus	Network analysis variant	Centrality measure (path-based)
Font seminal	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.
Àlies	WKGA, weighted KG analysis, confidence-weighted knowledge graph, weighted semantic network analysis	WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted)
Relacionats	6	6
Resum	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.