Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Сетевые модели диффузии× | Предсказание связей× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1927 (epidemiological compartmental); 2003 (social influence cascade) | 2003 |
| Автор метода≠ | Kermack & McKendrick (SIR/SIS, 1927); Kempe, Kleinberg & Tardos (Independent Cascade, 2003) | — |
| Тип≠ | Stochastic / deterministic simulation on graphs | Network inference task |
| Основополагающий источник≠ | Kermack, W.O. & McKendrick, A.G. (1927). A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A, 115(772), 700-721. DOI ↗ | 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. DOI ↗ |
| Другие названия | epidemic spreading models, compartmental models, influence propagation models, Ağ Yayılım Modelleri (SIR, SIS, Independent Cascade) | Bağlantı Tahmini (Link Prediction), missing link prediction, future link prediction, edge prediction |
| Связанные | 5 | 5 |
| Сводка≠ | Network diffusion models are a family of compartmental and probabilistic frameworks that simulate how information, disease, or innovation spreads across a connected system. Rooted in the mathematical epidemiology of Kermack and McKendrick (1927), the SIR and SIS models partition nodes into states and track transitions driven by contact rates and recovery probabilities. The Independent Cascade and Linear Threshold models, formalised by Kempe, Kleinberg, and Tardos (2003), extend this logic to social influence, modelling how activation propagates through a network one neighbour at a time. | 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. |
| ScholarGateНабор данных ↗ |
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