方法对比

	加权指数随机图模型	加权度中心性
领域	网络分析	网络分析
方法族	Machine learning	Machine learning
起源年份	2012	2004
提出者	Krivitsky, P. N.	Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.
类型	Statistical network model	Centrality measure for weighted networks
开创性文献	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.
别名	W-ERGM, valued ERGM, weighted p-star model, valued exponential random graph model	node strength, strength centrality, weighted node degree, WDC
相关	4	6
摘要	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.