Reconstructing Large Scale Production Networks
Ashwin Bhattathiripad, Vipin P Veetil

TL;DR
This paper presents a scalable algorithm to reconstruct large-scale firm-to-firm production networks using firm size and sectoral flow data, capturing key topological properties of real networks.
Contribution
The paper introduces a novel four-step algorithm combining gravity models, probabilistic graph construction, and convex optimization for large network reconstruction.
Findings
Successfully reconstructed the US production network with over 5 million firms.
The reconstructed network matches real-world properties like fat-tailed degree distribution and clustering.
The algorithm is computationally efficient and open-source.
Abstract
This paper develops an algorithm to reconstruct large weighted firm-to-firm networks using information about the size of the firms and sectoral input-output flows. Our algorithm is based on a four-step procedure. We first generate a matrix of probabilities of connections between all firms in the economy using an augmented gravity model embedded in a logistic function that takes firm size as mass. The model is parameterized to allow for the probability of a link between two firms to depend not only on their sizes but also on flows across the sectors to which they belong. We then use a Bernoulli draw to construct a directed but unweighted random graph from the probability distribution generated by the logistic-gravity function. We make the graph aperiodic by adding self-loops and irreducible by adding links between Strongly Connected Components while limiting distortions to sectoral…
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Taxonomy
TopicsEconomic and Technological Innovation · Complex Network Analysis Techniques · Game Theory and Applications
