Abstract
In economy, a major issue is the potential lack of liquidity for settling the debts generated by payment delays among companies. Since this lack may trigger cascading failures, we analyse the interconnection of debts. Settling debts means lowering the systemic risks. We analyse the data of a large economic network from an Italian invoice operator on a one-year span. We compare different methods to detect structures or communities that could be helpful for debt netting algorithms. The structure of such networks is not currently well known. We give hints on how to sort and identify the type of B2B invoice graphs. In particular, we address the possibility to identify relevant communities in such networks.
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Notes
- 1.
More information are available in an extended version on the French preprint server HAL.
- 2.
Different visuals are available in an extended version on HAL.
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Guichon, J., Fatès, N., Contassot-Vivier, S., Amato, M. (2024). Properties of B2B Invoice Graphs and Detection of Structures. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-031-53472-0_37
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