השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| אלגוריתמים לגילוי סיבתי (PC, FCI, LiNGAM)× | זיהוי קהילות× | |
|---|---|---|
| תחום≠ | הסקה סיבתית | ניתוח רשתות |
| משפחה≠ | Regression model | Process / pipeline |
| שנת המקור≠ | 2000 | 2002–2019 (algorithm family) |
| הוגה השיטה≠ | 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) |
| סוג≠ | Causal structure learning | Graph-partitioning / clustering algorithm family |
| מקור מכונן≠ | 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 ↗ |
| כינויים≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) |
| קשורות | 5 | 5 |
| תקציר≠ | 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? |
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