Inverse Complexity Lab

Organisms, ecosystems, climates, information systems, and societies are examples of complex systems: they exhibit rich behavior emerging from a large number of individual components following relatively simple local rules, based on a network of direct interactions.

Complexity science is traditionally devoted to solving the forward problem: Given postulated local rules of interaction, what is the large-scale organization that emerges from them?

Our research group focuses on the inverse problem: Given an empirically observed large-scale organization, what are the local rules of interaction that caused it?

With this aim, our group develops mathematical and computational models to explain the structure and function of complex network systems, and the algorithms to reconstruct the structure of these models from available empirical data.

We are particularly interested in problems related to:

  1. Statistically principled pattern identification in complex networks.
  2. Inverse problems and reconstruction of large-scale systems from indirect data.
  3. Uncertainty quantification for complex systems.
  4. Generative models that characterize modular hierarchies, ranks, and latent spaces.
  5. Temporality, higher-order organization, and multivariate annotation of relational data.
  6. Scalable network algorithms.
  7. Scientific software development and dataset curation.

In our work, we employ theory and methods from several disciplines, including statistical physics, computational statistics, information theory, Bayesian inference, and machine learning.

For more information see our research and publications pages.

NoteThe graph-tool library

Most of the methods developed in our group are made available as part of the graph-tool library, which is extensively documented.
For a practical introduction to many inference and reconstruction algorithms, please refer to the HOWTO.

Open positions

Prospective post-doc researchers should refer to the joining the group page.

Group news

References

[1]
T. P. Peixoto, L. Peel, T. Gross, and M. D. Domenico, Graphs Are Maximally Expressive for Higher-Order Interactions, arXiv:2602.16937 (2026).
[2]
W. Zhang, O. Boichak, T. J. Alexander, T. P. Peixoto, and E. G. Altmann, Meso-Scale Structures in Signed Networks, arXiv:2512.11281 (2025).
[3]
T. P. Peixoto, Uncertainty Quantification and Posterior Sampling for Network Reconstruction, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 481, 20250344 (2025).
[4]
T. P. Peixoto, Scalable Network Reconstruction in Subquadratic Time, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 481, 20250345 (2025).
[5]
T. Robiglio, M. Contisciani, M. Karsai, and T. P. Peixoto, Multiscale Patterns of Migration Flows in Austria: Regionalization, Administrative Barriers, and Urban-Rural Divides, arXiv:2507.11503 (2025).
[6]
T. P. Peixoto, Network Reconstruction via the Minimum Description Length Principle, Physical Review X 15, 011065 (2025).
[7]
[8]
T. P. Peixoto, Scalable Network Reconstruction in Subquadratic Time, arXiv:2401.01404 (2024).

Tiago de Paula Peixoto

Professor of Complex Systems and Network Science

Center for Critical Computational Studies (C³S)
Goethe University Frankfurt
Germany

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Short bio

Tiago P. Peixoto is Professor of Complex Systems and Network Science at the Center for Critical Computational Studies (C³S) and Institute of Computer Science, Goethe University Frankfurt, Germany. He is also a Fellow Professor at IT:U, and External Faculty at the Complexity Science Hub, Vienna, Austria.

He received his habilitation in theoretical physics at the University of Bremen in 2017. Previously, he was Full Professor at IT:U (2024-2026), Associate Professor at the Central European University (2019-2024), Assistant Professor in Applied Mathematics at the University of Bath (2016-2019), External Researcher at the ISI Foundation (2015-2020), and post-doc researcher at the University of Bremen (2011-2016) and Technical University of Darmstadt (2008-2011).

He received his PhD in Physics at the University of São Paulo in 2008.

His research group works at the interface between statistical physics, computational statistics, information theory, Bayesian inference, and machine learning, and has as its main focus the study of inverse problems in network science and complex systems.

His work was recognized with the Erdős–Rényi Prize from the Network Science Society in 2019.

He also received a Alexander von Humbolt Foundation fellowship in 2008.

And most importantly, he is the proud 6th recipient of the distinguished Karate Club Club prize. 🥋🏆

Tiago (left) receiving the notorious Karate Club Club trophy from Travis Martin (right), on behalf of the 5th recipient, Mark Newman, in 2015.