手法を比較

	重み付き指数型ランダムグラフモデル	重み付き社会ネットワーク分析
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	2004–2010
提唱者	Krivitsky, P. N.	Barrat, A.; Opsahl, T. et al.
種類	Statistical network model	Network analysis framework
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	Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis
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 Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships.