Сравнение методов

	Направленная экспоненциальная модель случайных графов	Анализ направленных социальных сетей
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	1986 (foundations); 2007 (modern directed ERGM formulation)	1994
Автор метода	Frank, O. & Strauss, D.; extended by Robins, Pattison, Kalish & Lusher	Wasserman, S. & Faust, K.
Тип	Statistical generative model for directed networks	Structural analysis of directed graphs
Основополагающий источник	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.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Другие названия	Directed ERGM, p-star model (directed), directed p* model, directed Markov graph model	directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis
Связанные	4	5
Сводка	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 Social Network Analysis (directed SNA) studies networks in which every tie has an explicit direction — from a sender to a receiver — rather than treating relationships as symmetric. It extends the classical SNA toolkit with in-degree, out-degree, reciprocity, and asymmetric path measures, making it the appropriate framework wherever relationship direction carries substantive meaning, such as citation flows, advice-seeking, follower graphs, or information cascades.