Сравнение методов

	Взвешенная экспоненциальная модель случайных графов	Взвешенная степень центральности
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2012	2004
Автор метода	Krivitsky, P. N.	Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.
Тип	Statistical network model	Centrality measure for weighted networks
Основополагающий источник	Krivitsky, P. N. (2012). Exponential-family random graph models for valued networks. Electronic Journal of Statistics, 6, 1100–1128.	Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.
Другие названия	W-ERGM, valued ERGM, weighted p-star model, valued exponential random graph model	node strength, strength centrality, weighted node degree, WDC
Связанные	4	6
Сводка	The Weighted Exponential Random Graph Model (W-ERGM) extends the classic binary ERGM framework to networks whose edges carry quantitative values — such as frequency of contact, trade volume, or collaboration intensity. It models the entire valued-edge network as a probability distribution defined over all possible weighted graphs, enabling researchers to test whether structural patterns such as reciprocity, transitivity, or degree distribution arise beyond what chance alone would produce.	Weighted degree centrality — also called node strength — extends the classic degree centrality measure to networks whose edges carry numeric weights. Instead of simply counting a node's connections, it sums the weights of all edges incident to that node, capturing both the volume and the intensity of a node's ties in a single, interpretable score.