Сравнение методов

	k-ядерная декомпозиция	Анализ центральности	Центральность PageRank
Область	Сетевой анализ	Сетевой анализ	Сетевой анализ
Семейство	Process / pipeline	Process / pipeline	Machine learning
Год появления	1983	1979	1999
Автор метода	Stephen B. Seidman	Linton C. Freeman	Page, Brin, Motwani & Winograd
Тип	Graph pruning and hierarchical decomposition	Descriptive / exploratory network measure family	Iterative link-based centrality algorithm
Основополагающий источник	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.	Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.
Другие названия	Core Decomposition, Coreness Decomposition, Shell Decomposition, Çekirdek Ayrıştırma	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality	Google PageRank, Random Surfer Model, Link-Based Ranking, PageRank Merkeziliği
Связанные	3	5	2
Сводка	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.	PageRank is a link-based centrality algorithm that assigns an importance score to each node in a directed graph by measuring how many high-quality nodes point to it. Introduced by Larry Page, Sergey Brin, Rajeev Motwani, and Terry Winograd at Stanford University in 1999, it became the mathematical foundation of the Google search engine and remains one of the most influential algorithms in network science and information retrieval.