Σύγκριση μεθόδων

	Δυναμική Ανίχνευση Κοινοτήτων	Μοντέλο Στοχαστικών Τμημάτων
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Process / pipeline
Έτος προέλευσης	2010 (key formalization); earlier work 2002–2009	1983
Δημιουργός	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)	—
Τύπος	Graph clustering / community discovery	Probabilistic generative graph model
Θεμελιώδης πηγή	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.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Εναλλακτικές ονομασίες	DCD, temporal community detection, evolving community detection, dynamic graph clustering	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Συναφείς	5	7
Σύνοψη	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.	The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis.