方法对比

	有向指数随机图模型	随机块模型
领域	网络分析	网络分析
方法族	Machine learning	Process / pipeline
起源年份	1986 (foundations); 2007 (modern directed ERGM formulation)	1983
提出者	Frank, O. & Strauss, D.; extended by Robins, Pattison, Kalish & Lusher	—
类型	Statistical generative model for directed networks	Probabilistic generative graph model
开创性文献	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.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
别名	Directed ERGM, p-star model (directed), directed p* model, directed Markov graph model	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
相关	4	7
摘要	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.	The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis.