Juan Nelson Martínez Dahbura, Shota Komatsu, Takanori Nishida, Angelo Mele
arXiv 26 May 2021 · Econometrics
arXiv:2105.12704 · PDF · DOI · OpenAlex · Extracted main text
Social and professional networks affect labor market dynamics, knowledge diffusion and new business creation. To understand the determinants of how these networks are formed in the first place, we analyze a unique dataset of business cards exchanges among a sample of over 240,000 users of the multi-platform contact management and professional social networking tool for individuals Eight. We develop a structural model of network formation with strategic interactions, and we estimate users' payoffs that depend on the composition of business relationships, as well as indirect business interactions. We allow heterogeneity of users in both observable and unobservable characteristics to affect how relationships form and are maintained. The model's stationary equilibrium delivers a likelihood that is a mixture of exponential random graph models that we can characterize in closed-form. We overcome several econometric and computational challenges in estimation, by exploiting a two-step estimation procedure, variational approximations and minorization-maximization methods. Our algorithm is scalable, highly parallelizable and makes efficient use of computer memory to allow estimation in massive networks. We show that users payoffs display homophily in several dimensions, e.g. location; furthermore, users unobservable characteristics also display homophily.
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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 | Babkin, Stewart, Long \ Schweinberger (2020) Large-scale estimation of random graph models with local dependence | 1.000 | 15 | 3 | 100% |
| 2 | Mele (2020) A structural model of homophily and clustering in social networks self | 1.000 | 13 | 4 | 100% |
| 3 | Vu, Hunter \ Schweinberger (2013) `Model-based clustering of large networks', The Annals of Applied Statistics 7(2), 1010 – 1039 | 1.000 | 11 | 4 | 100% |
| 4 | Boucher \ Mourifie (2017) `My friend far far away: A random field approach to exponential random graph models', Econometrics Journal 20(3), S14–S46 | 1.000 | 9 | 3 | 100% |
| 5 | Schweinberger \ Handcock (2015) `Local dependence in random graph models: char- acterization, properties and statistical inference.', Journal of the Royal Stati… | 1.000 | 7 | 3 | 100% |
| 6 | Mele (2017) `A structural model of dense network formation', Econometrica 85(2), 825–850 self | 1.000 | 6 | 3 | 100% |
| 7 | Bickel, Choi, Chang \ Zhang (2013) `Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels', The Annals of Statist… | 0.874 | 5 | 2 | 100% |
| 8 | Mele \ Zhu `Approximate variational estimation for a model of network formation', Review of Economics and Statistics | 0.874 | 5 | 2 | 100% |
| 9 | Graham \ dePaula (2020) The econometric analysis of network data, Amsterdam: Academic Press, 2020 | 0.811 | 4 | 2 | 100% |
| 10 | Wainwright \ Jordan (2008) `Graphical models, exponential families, and variational inference', Foundations and Trends@ in Machine Learning 1(1-2), 1–305 | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 48 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Vulnerability Webs: Systemic Risk in Software Networks | 0.894 | 7 | 4 |