เปรียบเทียบวิธี

	Eigenvector Centrality	PageRank Centrality
สาขาวิชา	การวิเคราะห์เครือข่าย	การวิเคราะห์เครือข่าย
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	1972	1999
ผู้ริเริ่ม	Bonacich, P.	Page, Brin, Motwani & Winograd
ประเภท	Centrality measure	Iterative link-based centrality algorithm
แหล่งต้นตำรับ	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.	Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.
ชื่อเรียกอื่น	eigenvector centrality, EC, Bonacich centrality, power centrality	Google PageRank, Random Surfer Model, Link-Based Ranking, PageRank Merkeziliği
ที่เกี่ยวข้อง	6	2
สรุป	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.	PageRank is a link-based centrality algorithm that assigns an importance score to each node in a directed graph by measuring how many high-quality nodes point to it. Introduced by Larry Page, Sergey Brin, Rajeev Motwani, and Terry Winograd at Stanford University in 1999, it became the mathematical foundation of the Google search engine and remains one of the most influential algorithms in network science and information retrieval.