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	Model sieci losowych o wykładniczym rozkładzie (ERGM / p*)	Analiza sieciowa tekstu
Dziedzina	Analiza sieci	Eksploracja tekstu
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1986 (foundational); modern ERGM framework 1996–2007	2011 (Paranyushkin); 2005 (Diesner & Carley)
Twórca	Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)	Dmitry Paranyushkin; Jana Diesner & Kathleen M. Carley
Typ	Probabilistic generative network model	Text-mining network method
Źródło pierwotne	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.	Paranyushkin, D. (2011). Identifying the Pathways for Meaning Circulation Using Text Network Analysis. Nodus Labs.
Inne nazwy	ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*)	semantic network analysis, word co-occurrence network, Metin Ağ Analizi (Text Network Analysis)
Pokrewne	6	4
Podsumowanie	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.	Text network analysis models the words or concepts in a text as nodes and their co-occurrences as edges, then uses network metrics to reveal the structure of meaning. The approach was advanced by Diesner and Carley (2005) for communication networks and by Paranyushkin (2011) for tracing the pathways of meaning circulation in text.