Compară metode

	Detecția Comunităților	DBSCAN
Domeniu	Analiza rețelelor	Învățare automată
Familie	Process / pipeline	Machine learning
Anul apariției	2002–2019 (algorithm family)	1996
Autorul original	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
Tip	Graph-partitioning / clustering algorithm family	Density-based clustering algorithm
Sursa seminală	Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.
Denumiri alternative	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
Înrudite	5	3
Rezumat	Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.