Comparer des méthodes

	Modèle dirigé de graphe aléatoire exponentiel	Détection dirigée de communautés
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	1986 (foundations); 2007 (modern directed ERGM formulation)	2008
Auteur d'origine	Frank, O. & Strauss, D.; extended by Robins, Pattison, Kalish & Lusher	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.
Type	Statistical generative model for directed networks	Graph partitioning / modularity optimization
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.	Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.
Alias	Directed ERGM, p-star model (directed), directed p* model, directed Markov graph model	directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning
Apparentées	4	6
Résumé	The Directed Exponential Random Graph Model (Directed ERGM) is a family of statistical models for directed networks that estimates the probability of observing a given directed graph as a function of structural configurations — such as reciprocity, transitive triads, and in-degree centralization — and node or dyad covariates, enabling principled inference about the social processes that generate directed ties.	Directed community detection identifies densely interconnected groups of nodes in a directed network, accounting for the asymmetry of edges (e.g., A follows B does not imply B follows A). Adapting modularity or flow-based criteria to directed graphs reveals clusters that undirected methods systematically miss, making it essential for citation networks, follower graphs, and biological regulatory pathways.