Marzio Di Vece, Diego Garlaschelli, Tiziano Squartini
arXiv 3 Oct 2022 · physics.soc-ph · publishedChaos Solitons & Fractals (2022) · 3 citations (OpenAlex)
arXiv:2210.01179 · PDF · DOI · OpenAlex · Extracted main text
In the study of economic networks, econometric approaches interpret the traditional Gravity Model specification as the expected link weight coming from a probability distribution whose functional form can be chosen arbitrarily, while statistical-physics approaches construct maximum-entropy distributions of weighted graphs, constrained to satisfy a given set of measurable network properties. In a recent, companion paper, we integrated the two approaches and applied them to the World Trade Web, i.e. the network of international trade among world countries. While the companion paper dealt only with discrete-valued link weights, the present paper extends the theoretical framework to continuous-valued link weights. In particular, we construct two broad classes of maximum-entropy models, namely the integrated and the conditional ones, defined by different criteria to derive and combine the probabilistic rules for placing links and loading them with weights. In the integrated models, both rules follow from a single, constrained optimization of the continuous Kullback-Leibler divergence; in the conditional models, the two rules are disentangled and the functional form of the weight distribution follows from a conditional, optimization procedure. After deriving the general functional form of the two classes, we turn each of them into a proper family of econometric models via a suitable identification of the econometric function relating the corresponding, expected link weights to macroeconomic factors. After testing the two classes of models on World Trade Web data, we discuss their strengths and weaknesses.
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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 | M. Di Vece, D. Garlaschelli and T. Squartini, Gravity models of netw… (2022) self | 0.961 | 9 | 5 | 89% |
| 2 | D. Garlaschelli and M. I. Loffredo, Fitness-Dependent Topological Pr… (2004) | 0.874 | 5 | 2 | 100% |
| 3 | D. Garlaschelli and M. I. Loffredo, Structure and evolution of the w… (2005) | 0.874 | 5 | 2 | 100% |
| 4 | F. Parisi, T. Squartini and D. Garlaschelli, A faster horse on a saf… (2020) | 0.737 | 3 | 3 | 67% |
| 5 | E. T. Jaynes, Information Theory and Statistical Mechanics, Phys. Re… (1957) | 0.644 | 2 | 2 | 100% |
| 6 | E. T. Jaynes, Information Theory and Statistical Mechanics II, Phys.… (1957) | 0.644 | 2 | 2 | 100% |
| 7 | E. T. Jaynes, On the rationale of maximum-entropy methods, Proceedin… (1982) | 0.644 | 2 | 2 | 100% |
| 8 | R. Mastrandrea, T. Squartini, G. Fagiolo and D. Garlaschelli, Recons… (2014) self | 0.644 | 2 | 2 | 100% |
| 9 | C. E. Shannon, A mathematical theory of communication,The Bell syste… (1948) | 0.644 | 2 | 2 | 100% |
| 10 | T. Squartini, G. Fagiolo and D. Garlaschelli, Randomizing world trad… (2011) | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 59 scored citations.