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	Model sieci losowych o wykładniczym rozkładzie (ERGM / p*)	Algorytmy odkrywania przyczynowości (PC, FCI, LiNGAM)	DBSCAN	Sieć uwagi grafowej
Dziedzina	Analiza sieci	Wnioskowanie przyczynowe	Uczenie maszynowe	Uczenie głębokie
Rodzina	Process / pipeline	Regression model	Machine learning	Machine learning
Rok powstania	1986 (foundational); modern ERGM framework 1996–2007	2000	1996	2018
Twórca	Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Veličković, P. et al.
Typ	Probabilistic generative network model	Causal structure learning	Density-based clustering algorithm	Graph neural network (attention-based)
Źródło pierwotne	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	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.
Inne nazwy	ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*)	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
Pokrewne	6	5	3	4
Podsumowanie	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.	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).