השוואת שיטות

	מרכזיות קרבה	מרכזיות PageRank
תחום	ניתוח רשתות	ניתוח רשתות
משפחה	Machine learning	Machine learning
שנת המקור	1950 (formalized 1979)	1999
הוגה השיטה	Bavelas, A.; formalized by Freeman, L. C.	Page, Brin, Motwani & Winograd
סוג	Node-level centrality index	Iterative link-based centrality algorithm
מקור מכונן	Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.	Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.
כינויים	closeness, farness-based centrality, geodesic closeness, normalized closeness centrality	Google PageRank, Random Surfer Model, Link-Based Ranking, PageRank Merkeziliği
קשורות	6	2
תקציר	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.	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.