Wenqin Du, Bailey K. Fosdick, Wen Zhou
arXiv 16 Feb 2025 · Statistics — Methodology
arXiv:2502.11255 · PDF · DOI · OpenAlex · Extracted main text
Relational data characterized by directed edges with count measurements are common in social science. Most existing methods either assume the count edges are derived from continuous random variables or model the edge dependency by parametric distributions. In this paper, we develop a latent multiplicative Poisson model for relational data with count edges. Our approach directly models the edge dependency of count data by the pairwise dependence of latent errors, which are assumed to be weakly exchangeable. This assumption not only covers a variety of common network effects, but also leads to a concise representation of the error covariance. In addition, the identification and inference of the mean structure, as well as the regression coefficients, depend on the errors only through their covariance. Such a formulation provides substantial flexibility for our model. Based on this, we propose a pseudo-likelihood based estimator for the regression coefficients, demonstrating its consistency and asymptotic normality. The newly suggested method is applied to a food-sharing network, revealing interesting network effects in gift exchange behaviors.
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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 | Graham, B. S (2020) Dyadic regression | 1.000 | 6 | 3 | 100% |
| 2 | Marrs, F. W., Fosdick, B. K., and McCormick, T. H (2023) Regression of exchangeable relational arrays self | 0.928 | 4 | 3 | 100% |
| 3 | Koster, J. M. and Leckie, G (2014) Food sharing networks in lowland Nicaragua: an application of the social relations model to count data | 0.874 | 15 | 2 | 100% |
| 4 | Gourieroux, C., Monfort, A., and Trognon, A (1984) Pseudo maximum likelihood methods: theory | 0.843 | 3 | 3 | 100% |
| 5 | Graham, B. S (2020) Network data | 0.843 | 3 | 3 | 100% |
| 6 | Kenny, D. A. and La Voie, L (1984) The social relations model | 0.843 | 3 | 3 | 100% |
| 7 | Hoff, P. D., Raftery, A. E., and Handcock, M. S (2002) Latent space approaches to social network analysis | 0.737 | 3 | 2 | 100% |
| 8 | Hoff, P (2021) Additive and multiplicative effects network models | 0.737 | 3 | 2 | 100% |
| 9 | Pensia, A., Jog, V., and Loh, P.-L (2019) Estimating location parameters in entangled single-sample distributions | 0.737 | 3 | 2 | 100% |
| 10 | Silverman, B. W (1976) Limit theorems for dissociated random variables | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 72 scored citations.