Comparer des méthodes

	Modèle dirigé de graphe aléatoire exponentiel	Analyse de modularité dirigée
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.
Type	Statistical generative model for directed networks	Community detection / graph partitioning
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 community detection via modularity, directed Q-modularity, digraph modularity optimization, Leicht-Newman modularity
Apparentées	4	5
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 modularity analysis extends the classic Newman-Girvan modularity framework to directed graphs, where edges carry a source and a destination. Formalized by Leicht and Newman in 2008, it partitions nodes into communities by maximizing a modularity score that accounts for each node's separate in-degree and out-degree in the null model, making it the standard approach for community detection in citation networks, information flows, and other asymmetric relational data.