手法を比較

	重み付き指数型ランダムグラフモデル	加重度中心性
分野	ネットワーク分析	ネットワーク分析
系統	Machine learning	Machine learning
提唱年	2012	2004
提唱者	Krivitsky, P. N.	Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.
種類	Statistical network model	Centrality measure for weighted networks
原典	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.
別名	W-ERGM, valued ERGM, weighted p-star model, valued exponential random graph model	node strength, strength centrality, weighted node degree, WDC
関連	4	6
概要	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.