Σύγκριση μεθόδων

	Χρονική Κεντρικότητα Βαθμού	Χρονική Ενδιάμεση Κεντρικότητα
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2011–2012	2012
Δημιουργός	Holme, P.; Saramaki, J.; Kim, H.; Anderson, R.	Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.
Τύπος	Centrality measure (temporal extension)	Centrality measure for temporal networks
Θεμελιώδης πηγή	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Εναλλακτικές ονομασίες	time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC	TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness
Συναφείς	6	6
Σύνοψη	Temporal degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window.	Temporal Betweenness Centrality (TBC) extends classical betweenness centrality to time-stamped networks by counting how often a node lies on time-respecting shortest paths — paths that traverse edges in chronological order. It identifies nodes that act as temporal brokers, controlling information or resource flow as it evolves over time, rather than in a static snapshot.