Σύγκριση μεθόδων

	Δυναμική Κεντρικότητα Βαθμού	Δυναμική Ανίχνευση Κοινοτήτων
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2012	2010 (key formalization); earlier work 2002–2009
Δημιουργός	Holme, P. & Saramaki, J.; Kim, H. & Anderson, R.	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)
Τύπος	Centrality measure (temporal extension)	Graph clustering / community discovery
Θεμελιώδης πηγή	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.
Εναλλακτικές ονομασίες	time-varying degree centrality, temporal degree centrality, evolving degree centrality, DDC	DCD, temporal community detection, evolving community detection, dynamic graph clustering
Συναφείς	5	5
Σύνοψη	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.	Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research.