Comparer des méthodes

	Modèle exponentiel de graphes aléatoires (ERGM / p*)	Réseau d'attention sur graphe
Domaine	Analyse de réseaux	Apprentissage profond
Famille	Process / pipeline	Machine learning
Année d'origine	1986 (foundational); modern ERGM framework 1996–2007	2018
Auteur d'origine	Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)	Veličković, P. et al.
Type	Probabilistic generative network model	Graph neural network (attention-based)
Source fondatrice	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.	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.
Alias	ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*)	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
Apparentées	6	4
Résumé	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.	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).