Сравнение методов

	Анализ центральности	Предсказание связей	Анализ устойчивости и уязвимости сетей
Область	Сетевой анализ	Сетевой анализ	Сетевой анализ
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	1979	2003	2000
Автор метода	Linton C. Freeman	—	Albert, Jeong & Barabási
Тип	Descriptive / exploratory network measure family	Network inference task	Network robustness / vulnerability framework
Основополагающий источник	Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239.	Liben-Nowell, D. & Kleinberg, J. (2007). The Link-Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology, 58(7), 1019-1031.	Albert, R., Jeong, H. & Barabási, A.L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382.
Другие названия	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality	Bağlantı Tahmini (Link Prediction), missing link prediction, future link prediction, edge prediction	network vulnerability analysis, attack tolerance analysis, Ağ Dayanıklılığı ve Güvenlik Açığı Analizi
Связанные	5	5	5
Сводка	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.	Link prediction is a network-analysis task that estimates which edges are missing from an observed graph or which edges are likely to form in the future. Formalised by Liben-Nowell and Kleinberg (2003, 2007), it covers a spectrum of approaches — from simple structural similarity indices such as Common Neighbors, Jaccard coefficient, and Adamic-Adar, to matrix factorisation, and graph neural network (GNN) methods — and is evaluated with AUC and Average Precision to account for the heavily imbalanced ratio of real to non-existing edges.	Network resilience and vulnerability analysis is an analytical framework, formalised by Albert, Jeong, and Barabási (2000), that measures how a network degrades functionally as nodes or edges are progressively removed. By running targeted-attack simulations — removing the highest-centrality nodes first — and random-failure simulations — removing nodes at uniform probability — the framework identifies which structural elements are critical to network integrity and where infrastructure is most exposed.