مقایسهٔ روش‌ها

	الگوریتم‌های کشف علّی (PC, FCI, LiNGAM)	آشکارسازی جامعه	شبکه توجه گراف
حوزه	استنتاج علّی	تحلیل شبکه	یادگیری عمیق
خانواده	Regression model	Process / pipeline	Machine learning
سال پیدایش	2000	2002–2019 (algorithm family)	2018
پدیدآور	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)	Veličković, P. et al.
نوع	Causal structure learning	Graph-partitioning / clustering algorithm family	Graph neural network (attention-based)
منبع بنیادین	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.	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.
نام‌های دیگر	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
مرتبط	5	5	4
خلاصه	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?	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).