Linganisha mbinu

	Ukuaji wa Kati wa Shahada ya Nguvu	Uchanganuzi wa Mitandao ya Muda
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Process / pipeline
Mwaka wa asili	2012	2012
Mwanzilishi	Holme, P. & Saramaki, J.; Kim, H. & Anderson, R.	Holme & Saramäki (2012) — seminal framework
Aina	Centrality measure (temporal extension)	Dynamic graph analysis
Chanzo asilia	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.
Majina mbadala	time-varying degree centrality, temporal degree centrality, evolving degree centrality, DDC	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Zinazohusiana	5	3
Muhtasari	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.	Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.