Research

Taking the perspective that economies emerge out of many millions of transactions lets me ask compelling empirical and theoretical research questions. Most of my work follows from and contributes to this idea. I have also particpated in various projects as a part of various institutions.

Real-world walk processes & transaction data

People and companies leave a trail of transactions behind them as they go about their business. This means there is a treasure trove of data about the real economy sitting on the servers of payment processing institutions, 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 process on networks.

Mattsson, C. E. S., Criscione, T., & Takes, F. W. (2023). Circulation of a digital community currency. Scientific Reports, 13, Article number: 5864. DOI: 10.1038/s41598-023-33184-1

Mattsson, C. E. S., Criscione, T., & Ruddick, W. O. (2022). Sarafu Community Inclusion Currency 2020-2021. Scientific Data, 9(426). DOI: 10.1038/s41597-022-01539-4

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)

Mattsson, C. (preprint). Networks of monetary flow at native resolution. ArXiv:1910.05596 [Physics, q-Fin]. arxiv:1910.05596 Download (pdf)

Mattsson, C. E. S., Luedtke, A., & Takes, F. W. (preprint). Measuring the Velocity of Money. arXiv:2209.01512

Structure of 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. 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) | 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.

CENTAI

CENTAI is a newly established research institute located in Turin, Italy. The ambition of CENTAI is to push the boundaries of applied and foundational research in domains related to Artificial Intelligence and Complex System sciences.

Computational Network Science Lab

The Leiden Computational Network Science Lab (CNS Lab) is a research group working on methods for knowledge discovery from real-world network data. We develop methods and algorithms to unveil patterns in dynamic complex networks from a range of application domains, including (online) social networks, scientific collaboration networks and economic networks. The CNS Lab is lead by Frank Takes and affiliated with the Theory and Data Science clusters of the Department of Computer Science (LIACS) of Leiden University.

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). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611977653.ch1

Lazer Lab

The Lazer Lab advances methods in computational social science. I have contributed to several projects that aim to make digital trace and administrative data more useful to researchers.