Research

Economies emerge out of many millions of transactions. This perspective raises compelling empirical and theoretical research questions — most of my work follows from and leads back to this core idea. I have also contributed to various projects across network science and computational social science.

Real-world walk processes & transaction data

People and companies leave a scattered trail of transactions behind them as they go about their business. Sitting on the servers of financial institutions are a treasure troves of empirical data about the real economy, worldwide. On the applied side, I develop methods and tools that facilitate the analysis of such datasets. On the theoretical side, currency systems are a compelling example with which to advance a theory of real-world walk processes on networks – from activity-driven models of financial transactions to methods for measuring money velocity.

Mattsson, C., Cellerini, C., Ojer, J., & Starnini, M. (preprint). Modeling financial transactions via random walks on temporal networks. arXiv:2602.20713 | Code

Mattsson, C. E. S., Luedtke, A., & Takes, F. W. (preprint). Holding periods: Inverse estimation of money velocity. arXiv:2209.01512 | Working Paper (pdf)

Mattsson, C. E. S., Criscione, T., & Takes, F. W. (2023). Circulation of a digital community currency. Scientific Reports, 13, Article number: 5864. Open Access

Mattsson, C. E. S., & Takes, F. W. (2021). Trajectories through temporal networks. Applied Network Science, 6(1), 1–31. DOI: 10.1007/s41109-021-00374-7 | Paper (pdf) | Code

Mattsson, C. E. S., & Takes, F. W. (2021). Trajectories through temporal networks. Applied Network Science, 6(1), 1–31. Open Access | Paper (pdf)

Mattsson, C. E. S., Luedtke, A., & Takes, F. W. (preprint). Holding periods: Inverse estimation of money velocity. arXiv:2209.01512 | Code

Firm-level production networks

Companies are especially similar to their close competitors, not to their trading partners. This has practical implications for the analysis of production networks and give us precise terms for the local structural features that may be key to understanding their routine function, failure, and growth. It also places production networks into the same ‘‘category’’ of network as protein-protein interaction (PPI) networks, where our intutions from social networks can lead us astray.

Mattsson, C. (2025). Network growth under opportunistic attachment. Applied Network Science, 10:21. DOI: 10.1007/s41109-025-00706-x | Code

Mattsson, C. E. S., Takes, F. W., Heemskerk, E. M., Diks, C., Buiten, G., Faber, A., & Sloot, P. M. A. (2021). Functional Structure in Production Networks. Frontiers in Big Data, 4. DOI: 10.3389/fdata.2021.666712 | Paper (pdf) | Code | Slides (pdf) | Networks 2021 Winning Pitch Talk (mov)

Buiten, G., Diks, C., Faber, A., Heemskerk, E., Mattsson, C., Takes, F., & Sloot, P. (2021). Netwerkanalyse biedt nieuwe inzichten voor clustergericht energiebeleid. Economisch-Statistische Berichten, 106(4804), 586–588.

Other projects & collaborations

Moroni, C., Borile, C., Mattsson, C., Starnini, M., & Panisson, A. (2025). Multi-Class and Multi-Task Strategies for Neural Directed Link Prediction. Machine Learning and Knowledge Discovery in Databases, European Conference (ECML PKDD). DOI: 10.48550/arXiv.2412.10895

Mattsson, C. (2024). Computational social science with confidence. EPJ Data Science, 13:3. DOI: 10.1140/epjds/s13688-023-00435-0

Dieles, T. J., Mattsson, C., & Takes, F. W. (2024). Identifying successful teams in the European player transfer network. Applied Network Science, 9:65. DOI: 10.1007/s41109-024-00675-7

McNeil, M., Mattsson, C., Takes, F. W., & Bogdanov, P. (2023). CADENCE: Community-Aware Detection of Dynamic Network States. In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM) (pp. 1–9). DOI: 10.1137/1.9781611977653.ch1