השוואת שיטות

	מודל גרף אקראי מעריכי (ERGM / p*)	אלגוריתמים לגילוי סיבתי (PC, FCI, LiNGAM)	רשת קשב גרפית
תחום	ניתוח רשתות	הסקה סיבתית	למידה עמוקה
משפחה	Process / pipeline	Regression model	Machine learning
שנת המקור	1986 (foundational); modern ERGM framework 1996–2007	2000	2018
הוגה השיטה	Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Veličković, P. et al.
סוג	Probabilistic generative network model	Causal structure learning	Graph neural network (attention-based)
מקור מכונן	Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173-191.	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.
כינויים	ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*)	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
קשורות	6	5	4
תקציר	The Exponential Random Graph Model (ERGM), also known as the p* model, is a statistical framework for network analysis that models the probability of an observed network as a function of its local structural features — such as reciprocity, triangles, and degree distribution. Developed from the foundational work of Frank and Strauss (1986) and extended into the modern framework by Wasserman and Pattison (1996) and Robins et al. (2007), ERGM is the inferential standard for social network analysis, capable of testing whether observed network structures arise by chance or reflect genuine social processes.	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.	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).