Compară metode

	Analiza ponderată a grafurilor de cunoștințe	Analiza rețelelor multiplex
Domeniu	Analiza rețelelor	Analiza rețelelor
Familie	Machine learning	Machine learning
Anul apariției	2010s–present	2014
Autorul original	Hogan et al. and the broader knowledge graph community	Kivela, M.; Boccaletti, S. et al.
Tip	Network analysis variant	Structural network model
Sursa seminală	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.	Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.
Denumiri alternative	WKGA, weighted KG analysis, confidence-weighted knowledge graph, weighted semantic network analysis	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Înrudite	6	6
Rezumat	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.	Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities.