Σύγκριση μεθόδων

	Κεντρικότητα Χρονικής Εγγύτητας	Κεντρικότητα Εγγύτητας
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2011	1950 (formalized 1979)
Δημιουργός	Pan, R. K. & Saramaki, J.	Bavelas, A.; formalized by Freeman, L. C.
Τύπος	Centrality measure (temporal)	Node-level centrality index
Θεμελιώδης πηγή	Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105.	Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.
Εναλλακτικές ονομασίες	time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality	closeness, farness-based centrality, geodesic closeness, normalized closeness centrality
Συναφείς	6	6
Σύνοψη	Temporal closeness centrality extends the classical closeness measure to time-varying networks by replacing static shortest paths with time-respecting (foremost) paths. It quantifies how quickly a node can reach all other nodes when interactions occur at specific moments in time, giving a more realistic picture of information flow, disease spread, and influence in dynamic systems.	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.