Bandingkan metode

	Algoritma Penemuan Kausal (PC, FCI, LiNGAM)	Deteksi Komunitas	Jaringan Perhatian Graf
Bidang	Inferensi Kausal	Analisis Jaringan	Pembelajaran Mendalam
Keluarga	Regression model	Process / pipeline	Machine learning
Tahun asal	2000	2002–2019 (algorithm family)	2018
Pencetus	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.
Tipe	Causal structure learning	Graph-partitioning / clustering algorithm family	Graph neural network (attention-based)
Sumber perintis	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.
Alias	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
Terkait	5	5	4
Ringkasan	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).