Multiscale Reconstruction of Weighted Networks from Coarse-Grained Data

2026-09-29

AI Summary

This study addresses the challenge of accurately reconstructing fine-grained weighted networks when only coarse-grained observational data are available. To this end, we propose a probabilistic reconstruction framework grounded in multiscale modeling. By leveraging hierarchical coarse-graining techniques that preserve functional form invariance, the method transfers global parameters from observable aggregated layers to finer scales. This enables network reconstruction across arbitrary aggregation levels without refitting parameters at the target resolution, thereby overcoming the limitations of conventional approaches that rely on same-scale information. Experiments on international trade and Dutch production networks demonstrate that the proposed framework achieves high-fidelity recovery of fine-grained structures even under severely restricted information conditions, substantially improving reconstruction accuracy.

Abstract

Network reconstruction from partial information is usually performed at the same resolution level at which constraints are observable. This becomes problematic when only coarse-grained information is available, while the relevant process occurs at a finer scale. Here we employ the multiscale model of weighted networks introduced in a companion paper and turn it into a probabilistic framework for reconstructing weighted networks across arbitrary aggregation levels. The model is built to preserve its functional form under coarse-graining, so that global parameters calibrated on an observable aggregate layer can be transferred to finer layers without refitting. We test the method on two empirical systems with different aggregation mechanisms. In the International Trade Network, countries are aggregated into geographic macro-regions and the observed coarse-grained layer is used to infer the underlying country-level network. In the Dutch production network, sectoral flows are reconstructed across the hierarchical industrial classification, using coarser sectoral layers to infer finer ones. In both geographical and sectoral settings, we benchmark our genuinely multiscale reconstruction method against a state-of-the-art weighted reconstruction model calibrated directly at the target resolution, and therefore using additional information available at the same (finer) scale at which performance is evaluated. Remarkably, despite this informational disadvantage, our method recovers the fine-scale binary structure with high accuracy, improving precision, specificity, accuracy and maximum degree-error diagnostics, while remaining nearly equivalent in sensitivity.

Problem

• network reconstruction
• weighted networks
• coarse-grained data
• multiscale

Innovation

• Multiscale reconstruction
• Weighted networks
• Coarse-grained data
• Probabilistic framework
• Network inference