About me

Marco Mancastroppa



I am a physicist with a background in statistical physics and physics of complex systems. My research activity focuses on the study of complex networks, higher-order interactions, network dynamics and stochastic processes on networks, with applications in epidemiology, social systems, and data-driven modeling.

I am currently a post-doctoral researcher at the Centre de Physique Théorique (CNRS, Aix-Marseille Université) in Marseille (France) under the supervision of Alain Barrat.

Here you can find information about my academic trajectory, my research projects, my collaboration network and publications. Feel free to reach me out if you’re interested in potential collaborations!

Topics

Structural characterization of hypergraphs

hyper Going beyond networks, to include higher-order interactions, is a major step to better describe complex systems characterized by group interactions. In the resulting hypergraph representation, tools to identify structures and central nodes are scarce. We are working on developing a series of methods specifically designed to analyse the topological properties of hypergraphs at multiple scales.

- Mancastroppa M. et al, Nat Commun 14, 6223 (2023)
- Agostinelli C. et al, arXiv:2503.16959 (2025)
- Mancastroppa M. et al, EPJ Data Science 13, 50 (2024)

Complex dynamic processes

OP Complex dynamic processes are based on intrinsically higher-order mechanisms, where multiple exposure and group reinforcement are active, requiring to consider multi-body interactions. Higher-order interactions give rise to both novel structures and phenomena, deeply affecting dynamical processes. We are interested in investigating how the higher-order structure of the interactions affects the dynamical processes and how these differ from simple dynamics on pairwise networks.

- Mancastroppa M. et al, Nat Commun 14, 6223 (2023)
- Cencetti G. et al, PRL 130, 247401 (2023)

Temporal hypergraphs generation

Ht Temporal hypergraphs represent a powerful framework to describe complex systems composed of elements interacting in groups, with interactions evolving over time. Designing models of temporal hypergraphs is crucial for generating surrogates of real observed dynamics, and also for understanding the role of topological and temporal properties on dynamical processes. We work on building new models of higher-order networks replicating features and mechanisms observed in empirical systems.

- Mancastroppa M. et al, arXiv:2507.01124 (2025)
- Mancastroppa M. et al, EPJ Data Science 13, 50 (2024)

Adaptive temporal networks

adaptive_temp Adaptive temporal networks represent a powerful paradigm for modelling the spread of epidemics, accounting for the coupling between epidemic processes and social interactions: human interactions are continuously rearranged over time, affecting the epidemic; the epidemic induces adaptive behaviours with which the population responds to the spread of the pathogen, affecting the social dynamics. Our work consists in understanding the interplay between epidemic dynamics and adaptive behaviors, which is essential to improve response strategies to epidemics.

- Mancastroppa M. et al, PRResearch 6, 033159 (2024)
- Mancastroppa M. et al, PRE 102, 020301(R) (2020)

Contact tracing for epidemic mitigation

CT Isolation of symptomatic individuals, tracing and testing of their nonsymptomatic contacts are fundamental adaptive behaviours that populations exposed to epidemics can implement. Moreover, effective contact tracing is crucial to containing epidemic spreading without disrupting societal activities. Within the framework of adaptive temporal networks, we investigate the effectiveness of different contact tracing strategies in curbing the epidemic and keeping the population activity.

- Mancastroppa M. et al, Nat Commun 12, 1919 (2021)
- Mancastroppa M. et al, J. R. Soc. Interface 19, 20220048 (2022)

Dynamical processes on temporal networks

Dyn_temp Many complex systems present time-varying interactions, which follow specific dynamics: these systems are represented using temporal networks. Dynamic processes on temporal networks are strongly impacted by the network dynamics, especially when the dynamics of and on the network have comparable time scales. We are interested in how temporal properties of the network influence dynamic processes unfolding upon it.

- Mancastroppa M. et al, Journal of Statistical Mechanics: Theory and Experiment 053502 (2019)

My collaboration network