So sánh phương pháp

	Eigenvector Centrality	Độ trung tâm gần (Closeness Centrality)
Lĩnh vực	Phân tích mạng lưới	Phân tích mạng lưới
Họ	Machine learning	Machine learning
Năm ra đời	1972	1950 (formalized 1979)
Người khởi xướng	Bonacich, P.	Bavelas, A.; formalized by Freeman, L. C.
Loại	Centrality measure	Node-level centrality index
Công trình gốc	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.	Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.
Tên gọi khác	eigenvector centrality, EC, Bonacich centrality, power centrality	closeness, farness-based centrality, geodesic closeness, normalized closeness centrality
Liên quan	6	6
Tóm tắt	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.	Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts.