Giulia Carallo, Roberto Casarin, Antonio Peruzzi
arXiv 7 Apr 2026 · Statistics — Methodology
arXiv:2604.05838 · PDF · DOI · OpenAlex · Extracted main text
Count-weighted temporal networks often exhibit unequal dispersion in the edge weights, which cannot be fully explained by modelling observational heterogeneity through latent factors in the conditional mean. Therefore, we propose new dynamic network model classes exploiting the Generalized Poisson distribution to capture both under- and overdispersion. We consider three different dynamic specifications: latent factor dynamics, autoregressive dynamics, and latent position dynamics, and study some theoretical properties of the random networks, showing the impact of the dispersion parameter on the random network's connectivity. After discussing the parameter identification strategy, we present a Bayesian inference procedure along with a posterior sampling algorithm. A numerical illustration demonstrates the effectiveness of the designed algorithm and provides estimates of the misspecification bias when unequal dispersion is neglected. Our new models are then applied to two relevant dynamic datasets considered in previous studies: a set of bike-sharing dynamic networks and a set of dynamic media networks. Our results highlight the importance of explicitly modeling overdispersion for both an accurate in-sample fit and out-of-sample performance.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Casarin, Roberto and Peruzzi, Antonio and Steel, Mark FJ (2025) Media bias and polarization through the lens of a Markov switching latent space network model self | 1.000 | 9 | 4 | 100% |
| 2 | Vershynin, Roman (2018) High-dimensional probability: An introduction with applications in data science | 1.000 | 8 | 3 | 100% |
| 3 | Schmidt, Ana Luc\'ia and Zollo, Fabiana and Scala, Antonio and Quatt… (2018) Polarization Rank: A Study on European News Consumption on Facebook | 1.000 | 7 | 4 | 100% |
| 4 | Citibike (2019) New York Citi Bike Data in August 2019 | 1.000 | 5 | 4 | 100% |
| 5 | Ambagaspitiya, R.S. and Balakrishnan, N (1994) On the Compound Generalized Poisson Distributions | 0.928 | 4 | 3 | 100% |
| 6 | Giulia Carallo and Roberto Casarin and Christian P. Robert (2024) Generalized Poisson difference autoregressive processes self | 0.928 | 4 | 3 | 100% |
| 7 | He, Yinqiu and Sun, Jiajin and Tian, Yuang and Ying, Zhiliang and Fe… (2025) Semiparametric modeling and analysis for longitudinal network data | 0.843 | 3 | 3 | 100% |
| 8 | Mezo, Istvan (2022) The Lambert W function: its generalizations and applications | 0.811 | 4 | 2 | 100% |
| 9 | Hoff, Peter D and Raftery, Adrian E and Handcock, Mark S (2002) Latent space approaches to social network analysis | 0.737 | 3 | 2 | 100% |
| 10 | Sewell, Daniel K and Chen, Yuguo (2016) Latent Space Models for Dynamic Networks with Weighted Edges | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 76 scored citations.