Сравнение на методи

	k-ядрена декомпозиция	Откриване на общности
Област	Мрежови анализ	Мрежови анализ
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1983	2002–2019 (algorithm family)
Създател	Stephen B. Seidman	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)
Тип	Graph pruning and hierarchical decomposition	Graph-partitioning / clustering algorithm family
Основополагащ източник	Seidman, S. B. (1983). Network structure and minimum degree. Social Networks, 5(3), 269–287.	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.
Други названия	Core Decomposition, Coreness Decomposition, Shell Decomposition, Çekirdek Ayrıştırma	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)
Свързани	3	5
Резюме	k-Core Decomposition is a graph-theoretic method that partitions the vertices of a network into a nested sequence of subgraphs called k-cores. A k-core is the maximal subgraph in which every vertex has at least k neighbors within that subgraph. Introduced by Stephen B. Seidman in 1983, the method assigns each vertex a coreness number that captures its structural centrality relative to the local connectivity of the graph.	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?