Bandingkan metode

	Sentralitas Derajat	Sentralitas Eigenvector
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	1978	1972
Pencetus	Freeman, L. C.	Bonacich, P.
Tipe	Node-level centrality measure	Centrality measure
Sumber perintis	Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.
Alias	node degree, degree score, DC, connectivity centrality	eigenvector centrality, EC, Bonacich centrality, power centrality
Terkait	6	6
Ringkasan	Degree centrality is the simplest and most intuitive measure of a node's importance in a network, defined as the number of direct ties a node has to other nodes. Normalized by dividing by the maximum possible ties, it allows comparison across networks of different sizes and is the starting point of almost every network analysis.	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.