विधियों की तुलना करें

	घातीय यादृच्छिक ग्राफ मॉडल (ईआरजीएम / पी*)	कार्यकारण खोज एल्गोरिदम (PC, FCI, LiNGAM)
क्षेत्र	नेटवर्क विश्लेषण	कारणात्मक अनुमान
परिवार	Process / pipeline	Regression model
उद्भव वर्ष	1986 (foundational); modern ERGM framework 1996–2007	2000
प्रवर्तक	Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)
प्रकार	Probabilistic generative network model	Causal structure learning
मौलिक स्रोत	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
उपनाम	ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*)	PC algorithm, FCI algorithm, LiNGAM, causal structure learning
संबंधित	6	5
सारांश	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.