Σύγκριση μεθόδων

	Κεντρικότητα PageRank	Ανάλυση Κεντρικότητας
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Process / pipeline
Έτος προέλευσης	1999	1979
Δημιουργός	Page, Brin, Motwani & Winograd	Linton C. Freeman
Τύπος	Iterative link-based centrality algorithm	Descriptive / exploratory network measure family
Θεμελιώδης πηγή	Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.	Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239.
Εναλλακτικές ονομασίες	Google PageRank, Random Surfer Model, Link-Based Ranking, PageRank Merkeziliği	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality
Συναφείς	2	5
Σύνοψη	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.	Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors.