Methoden vergleichen

	Weighted PageRank	Gewichtete Gradzentralität
Fachgebiet	Netzwerkanalyse	Netzwerkanalyse
Familie	Machine learning	Machine learning
Entstehungsjahr	2004	2004
Urheber	Xing, W. & Ghorbani, A.	Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.
Typ	Centrality measure / ranking algorithm	Centrality measure for weighted networks
Wegweisende Quelle	Xing, W., & Ghorbani, A. (2004). Weighted PageRank algorithm. Proceedings of the Second Annual Conference on Communication Networks and Services Research (CNSR '04), pp. 305–314. IEEE.	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.
Aliasnamen	WPR, weighted page rank, edge-weighted PageRank, strength-based PageRank	node strength, strength centrality, weighted node degree, WDC
Verwandt	6	6
Zusammenfassung	Weighted PageRank extends the classic PageRank algorithm to networks where edges carry different strengths or frequencies, distributing importance proportionally to both incoming and outgoing edge weights rather than treating all links equally. This makes it substantially more informative than binary PageRank in any network where connection strength matters.	Weighted degree centrality — also called node strength — extends the classic degree centrality measure to networks whose edges carry numeric weights. Instead of simply counting a node's connections, it sums the weights of all edges incident to that node, capturing both the volume and the intensity of a node's ties in a single, interpretable score.