Reconstruction of international energy trade networks with given marginal data: A comparative analysis

The energy commodity plays a significant role in economic development and national security. It is important for various countries to capture the linkages of the international energy trade network and avoid the occurrence of systemic risk. However, the granular data on trade network is often lacking, and instead, the network has to be reconstructed from partial data. In this paper, we compare seven network reconstruction methods by applying them to 16 types of energy trade data extracted from UN-Comtrade. To this end, first we present the topological structures and compute the dependency characteristics for these energy trade networks. Then, we conduct a horse race among different network reconstruction methods. The methods are ranked by comparing their abilities to reproduce the network structures and bilateral weights. Our findings show that Cimi is the winner across all 16 energy trade networks in terms of link-based similarity measures. In addition, the fitness models (Cimi and Musm) tend to perform best at reconstructing the weight matrix. These findings will aid in the accurate disclosure of latent international energy trade relations when only aggregated export and import figures are available, allowing systemic risks to be appropriately assessed.

Automated summary

The energy commodity is crucial for economic development and national security. Understanding the international energy trade network and avoiding systemic risk is important for countries. However, granular trade network data is often lacking, requiring network reconstruction from partial data. This paper compares seven network reconstruction methods applied to 16 types of energy trade data, finding that Cimi is the most successful method for reproducing network structures and bilateral weights, aiding in the assessment of systemic risks.