Linganisha mbinu

	Ukaribu wa Kati wa Nguvu (Dynamic Closeness Centrality)	Ukuaji wa Kati wa Shahada ya Nguvu
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2010–2012	2012
Mwanzilishi	Tang, J. et al.; Holme, P. & Saramäki, J.	Holme, P. & Saramaki, J.; Kim, H. & Anderson, R.
Aina	Centrality measure for temporal networks	Centrality measure (temporal extension)
Chanzo asilia	Tang, J., Musolesi, M., Mascolo, C., Latora, V. & Nicosia, V. (2010). Analysing information flows and key mediators through temporal centrality metrics. Proceedings of the 3rd Workshop on Social Network Systems (SNS '10). ACM.	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Majina mbadala	temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC	time-varying degree centrality, temporal degree centrality, evolving degree centrality, DDC
Zinazohusiana	5	5
Muhtasari	Dynamic closeness centrality extends classic closeness centrality to temporal networks by computing shortest time-respecting paths — paths that traverse edges in chronological order — and averaging inverse distances across all time windows. It reveals which nodes are most efficiently reached within an evolving network, tracking how a node's centrality rises and falls as connections appear and disappear over time.	Dynamic degree centrality extends the classical degree centrality measure to networks that change over time. Rather than counting a node's connections in a single static snapshot, it tracks how many contacts each node maintains across successive time windows or contact events, producing a time-resolved importance profile for every actor in the network.