So sánh phương pháp

	Trung tâm eigenvector có hướng	Phân tích mạng xã hội định hướng
Lĩnh vực	Phân tích mạng lưới	Phân tích mạng lưới
Họ	Machine learning	Machine learning
Năm ra đời	1972–1987	1994
Người khởi xướng	Bonacich, P.	Wasserman, S. & Faust, K.
Loại	Centrality measure (eigenvector-based, directed)	Structural analysis of directed graphs
Công trình gốc	Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Tên gọi khác	directed EC, asymmetric eigenvector centrality, right eigenvector centrality, left eigenvector centrality	directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis
Liên quan	5	5
Tóm tắt	Directed eigenvector centrality extends the classic eigenvector centrality to directed graphs by scoring each node according to the centrality of the nodes that point to it (in-direction) or that it points to (out-direction). A node earns a high score not merely by having many connections but by being connected to other highly central nodes, capturing asymmetric influence in citation networks, social hierarchies, and information flows.	Directed Social Network Analysis (directed SNA) studies networks in which every tie has an explicit direction — from a sender to a receiver — rather than treating relationships as symmetric. It extends the classical SNA toolkit with in-degree, out-degree, reciprocity, and asymmetric path measures, making it the appropriate framework wherever relationship direction carries substantive meaning, such as citation flows, advice-seeking, follower graphs, or information cascades.