Сравнение методов

	Предсказание связей	Стохастическая блочная модель
Область	Сетевой анализ	Сетевой анализ
Семейство	Process / pipeline	Process / pipeline
Год появления	2003	1983
Автор метода	—	—
Тип	Network inference task	Probabilistic generative graph model
Основополагающий источник	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.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Другие названия	Bağlantı Tahmini (Link Prediction), missing link prediction, future link prediction, edge prediction	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Связанные	5	7
Сводка	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.	The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis.