Comparer des méthodes

	Analyse de réseaux bipartis pondérés	Analyse de graphe de connaissances
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	1997 (two-mode); weighted extensions 2000s	2012–2016
Auteur d'origine	Borgatti, S. P. & Everett, M. G.	Ehrlinger, L. & Wöß, W.; Google (popularized)
Type	Network structural analysis	Graph-based knowledge representation and analysis
Source fondatrice	Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.	Ehrlinger, L. & Wöß, W. (2016). Towards a Definition of Knowledge Graphs. In Proceedings of the SEMANTICS Posters and Demos Track (SEMANTiCS 2016). CEUR Workshop Proceedings, vol. 1695.
Alias	weighted bipartite network analysis, valued two-mode network analysis, weighted affiliation network analysis, W2MNA	KG analysis, semantic graph analysis, knowledge base graph analysis, entity-relation graph analysis
Apparentées	6	5
Résumé	Weighted two-mode network analysis examines bipartite graphs in which two distinct node sets — such as actors and events, authors and papers, or species and habitats — are connected by edges carrying numerical weights that capture the strength, frequency, or intensity of each affiliation. Incorporating weights provides substantially richer structural insights than unweighted bipartite analysis.	Knowledge Graph Analysis is a framework for representing, storing, and reasoning over structured factual knowledge as a directed graph of entities and typed relations. Entities (nodes) and relationships (edges) are expressed as subject–predicate–object triples, enabling rich querying, inference, and integration of heterogeneous data sources across domains such as biomedical research, e-commerce, and scientific literature.