"Inference of epidemic networks: the effect of different data types" , new pre-print with Oscar Fajardo and Carl Suster. We introduce a model+MCMC to sample transmission trees based on different types of data on sampled hosts.

Paper: arxiv.org/abs/2509.01871
Code: github.com/oscarcapote/...

"A generative model for community types in directed networks", paper with Cathy X. Liu and Tristram J. Alexander, now published in the Journal of Complex Networks.

It shows how different relationships between two groups of nodes (users) can appear in (social) networks.

doi.org/10.1093/comn...

"Synthetic graphs for link prediction benchmarking", new paper with Alexey Vlaskin published in the Journal of Physics: Complexity @ioppublishing.bsky.social

doi.org/10.1088/2632...

Chapter 4 concludes the book with a general evaluation of the potential and limitations of statistical laws: a critical discussion of the uses and misuses in complex-systems research, connections to machine-learning approaches to data science, and speculations about the future of the field.

Chapter 3 critically discusses the different statistical methods used to evaluate the validity and estimate the parameters of statistical laws: from graphical models and linear regression to likelihood-based models and statistical inference.

Chapter 2 describes several cases of statistical laws, with focus on the identical role they play in different research areas: as connectors between data analysis and mechanistic models.

Chapter 1 starts from an historical account of paradigmatic laws, which motivate a general definition that: (i) includes examples from different disciplines and functional forms; (ii) distinguishes them from parametric fitting of curves or other statistical curiosities.

"Synthetic graphs for link prediction benchmarking" ,

New pre-print with Alexey Vlaskin

arxiv.org/abs/2412.03757