Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Алгоритмы каузального обнаружения (PC, FCI, LiNGAM)× | Обнаружение сообществ× | |
|---|---|---|
| Область≠ | Причинно-следственный вывод | Сетевой анализ |
| Семейство≠ | Regression model | Process / pipeline |
| Год появления≠ | 2000 | 2002–2019 (algorithm family) |
| Автор метода≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) |
| Тип≠ | Causal structure learning | Graph-partitioning / clustering algorithm family |
| Основополагающий источник≠ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 | Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008. DOI ↗ |
| Другие названия≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) |
| Связанные | 5 | 5 |
| Сводка≠ | Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges. | Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network? |
| ScholarGateНабор данных ↗ |
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