Bandingkan kaedah

	Pusat Darjah Berwajaran	Pusat Teras Eigenvector
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	2004	1972
Pengasas	Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.	Bonacich, P.
Jenis	Centrality measure for weighted networks	Centrality measure
Sumber perintis	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.	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.
Alias	node strength, strength centrality, weighted node degree, WDC	eigenvector centrality, EC, Bonacich centrality, power centrality
Berkaitan	6	6
Ringkasan	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.	Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network.