Citation Analysis

Estimating Network Models using Neural Networks\\ EXTENDED ABSTRACT
Angelo Mele
https://arxiv.org/abs/2502.01810

Citation mentions

Cited references

Sections

3,711

Words (approx)

References by Citation Intensity

Ordered by composite index (descending). Higher values indicate more intensive citation.

#	Reference	Year	Mentions	Breadth	Sec. Wtd	Share	Composite	Main %
1	Mele (self)	2017	3	2	4.0	0.143	0.737	100%
2	Chatterjee \harvardand\ Diaconis	2013	2	2	3.0	0.095	0.644	100%
3	Wei \harvardand\ Jiang	forthcoming	2	2	3.0	0.095	0.644	100%
4	Snijders	2002	1	1	1.0	0.048	0.406	100%
5	Robins, Pattison, Kalish \harvardand\ Lusher	2007	1	1	1.0	0.048	0.406	100%
6	Monderer \harvardand\ Shapley	1996	1	1	1.0	0.048	0.406	100%
7	DePaula	2017	1	1	1.0	0.048	0.406	100%
8	DePaula, Richards-Shubik \harvardand\ Tamer	2018	1	1	1.0	0.048	0.406	100%
9	Graham	2017	1	1	1.0	0.048	0.406	100%
10	Bhamidi, Bresler \harvardand\ Sly	2011	1	1	1.0	0.048	0.406	100%
11	Goodfellow, Bengio \harvardand\ Courville	2016	1	1	1.0	0.048	0.406	100%
12	Boucher \harvardand\ Mourifie	2017	1	1	2.0	0.048	0.406	100%
13	Geyer \harvardand\ Thompson	1992	1	1	2.0	0.048	0.406	100%
14	Murray, Ghahramani \harvardand\ MacKay	2006	1	1	2.0	0.048	0.406	100%
15	Caimo \harvardand\ Friel	2010	1	1	2.0	0.048	0.406	100%
16	Liang	2010	1	1	2.0	0.048	0.406	100%
17	Mele \harvardand\ Zhu	2023	1	1	2.0	0.048	0.406	100%

Measures: Mentions = total in-text citations; Breadth = distinct sections; Sec. Wtd = section-weighted count (body ×2, lit review/appendix ×0.5); Share = mentions / total citations in paper; Composite = geometric mean of normalised count, breadth, and main-text ratio; Main % = fraction of mentions in main text (excl. appendix). (self) = self-citation.