Порівняння методів

	k-Core Decomposition	Центральний аналіз
Галузь	Мережевий аналіз	Мережевий аналіз
Родина	Process / pipeline	Process / pipeline
Рік появи	1983	1979
Автор методу	Stephen B. Seidman	Linton C. Freeman
Тип	Graph pruning and hierarchical decomposition	Descriptive / exploratory network measure family
Основоположне джерело	Seidman, S. B. (1983). Network structure and minimum degree. Social Networks, 5(3), 269–287.	Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239.
Інші назви	Core Decomposition, Coreness Decomposition, Shell Decomposition, Çekirdek Ayrıştırma	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality
Пов'язані	3	5
Підсумок	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.	Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors.