Porovnať metódy

	Algoritmy kauzálneho objavovania (PC, FCI, LiNGAM)	Detekcia komunít	DBSCAN	Grafová at enčná sieť
Odbor	Kauzálna inferencia	Analýza sietí	Strojové učenie	Hlboké učenie
Rodina	Regression model	Process / pipeline	Machine learning	Machine learning
Rok vzniku	2000	2002–2019 (algorithm family)	1996	2018
Tvorca	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)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Veličković, P. et al.
Typ	Causal structure learning	Graph-partitioning / clustering algorithm family	Density-based clustering algorithm	Graph neural network (attention-based)
Pôvodný zdroj	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.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.
Ďalšie názvy	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
Príbuzné	5	5	3	4
Zhrnutie	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?	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN).