Overall, our work shows that both adaptivity and group interactions shape the structure of social ties and the global opinion dynamics in a population.
6/6
24.02.2026 12:56
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Overall, our work shows that both adaptivity and group interactions shape the structure of social ties and the global opinion dynamics in a population.
6/6
We show that adaptivity, which allows the formation of large groups, prevents the transition to a fragmented state. Moreover, it restores a phase transition from a polarized state to consensus, which would otherwise disappear due to group effects in a non-adaptive model with group interactions.
5/6
Strikingly, adaptivity seems to suppress important effects induced by group interactions, and to restore a phenomenology close to the one obtained with pairwise interactions (as also observed in higher-order contagion processes [ arxiv.org/abs/2601.05801 ]).
4/6
We propose a bounded confidence model of opinion dynamics taking into account these two mechanisms. A group discussion can lead to a global agreement among all group members, while a divergence of opinions leads to its splitting, followed by merging of the resulting subgroups with other groups.
3/6
Opinion dynamics models mimic how the opinions of individuals on a given topic may evolve when they interact. Discussions leading to opinion changes can occur in groups [ doi.org/10.1038/s420... ], and these groups can also undergo adaptive changes if their members disagree.
2/6
Now out our latest paper on adaptive opinion dynamics! 🚨
w/ Cosimo Agostinelli and @alainbarrat.bsky.social
arxiv.org/abs/2602.19684
How do group adaptive behaviors influence the emergence of polarization and consensus? How does group adaptation influence the structure of interactions?
1/6 🧵👇
Strategy evolution on temporal hypergraphs www.pnas.org/doi/abs/10.1...
🚨 The deadline for the early bird registration fee is coming up soon (March 1st). Make sure to complete your registration before! More info at: complenet.weeblysite.com/registration
Here's a short thread about it! 👇
bsky.app/profile/marc...
2/2
Our work on higher-order dissimilarity measures is now out in Journal of Complex Networks! 📣
academic.oup.com/comnet/artic...
with Cosimo Agostinelli and @alainbarrat.bsky.social
1/2
Our work provides insights into the effects of adaptive behaviors on contagion processes on hypergraphs. It highlights that considering higher-order interactions can lead to a different phenomenology than when risk perception is based on pairwise information. 7/7
Adaptive mechanisms driven by group interactions lead to a heterogeneous risk perception within the population, focusing on nodes with large hyperdegree, on their neighborhood, and on large groups. This prevents the spreading process to exploit the superspreading power of these nodes and groups. 6/7
We show that adaptive behaviors, based on higher-order information, are more effective in limiting the contagion spreading, than mechanisms based on pairwise information. Meanwhile, they also entail a lower social cost. 5/7
Here, we consider several adaptive behaviors driven by higher-order and pairwise information, and their impact on pairwise and higher-order contagion processes. 4/7
However, contagion and adaptation can also be driven by group interactions. We showed that adaptive behaviours have drastically different effects on the critical behavior of pairwise and higher-order contagion [ bsky.app/profile/marc... ]. 3/7
When exposed to a contagion phenomenon, individuals respond by adopting behavioral changes to reduce their exposure. Their impact on the contagion dynamics and on social activity has been investigated in pairwise networks [ www.sciencedirect.com/science/arti... ]. 2/7
Another new preprint on group adaptation! 📣
Great joint work w/ @martonkarsai.bsky.social and @alainbarrat.bsky.social!
Can adaptive behaviors driven by group interactions be more effective and less costly than pairwise ones? How do their adaptive mechanisms differ?
arxiv.org/abs/2602.05915
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Our work allows for a deeper understanding of higher-order processes on hypergraphs in the presence of adaptive behaviors, showing the non-trivial effects of integrating adaptive behaviors with higher-order interactions.
7/7
For higher-order contagion processes, instead, the adaptivity defuses the impact of non-linear group interactions: this reduces or even completely suppresses the bistability region, neutralizing the discontinuity and effectively transforming a higher-order contagion process into a pairwise one.
6/7
For pairwise contagion, adaptive mechanisms based on local (pairwise or group-based) risk perception impact only the endemic state, without affecting the epidemic phase transition, which remains continuous and with the same epidemic threshold.
5/7
Here, we consider the impact of several risk-based adaptive behaviors on pairwise and higher-order contagion processes, using numerical simulations and an analytical mean-field approach. In particular, we consider both pairwise-based and group-based adaptive mechanisms.
4/7
However, contagion and the perception of infection risk can also involve group interactions, leading potentially to new phenomenology [ doi.org/10.1038/s415... ]. How adaptive behavior resulting from risk perception affects higher-order processes remains an open question.
3/7
During contagion phenomena, individuals perceiving a risk of infection commonly adapt their behavior and reduce their exposure. The effects of such adaptive mechanisms have been studied for processes in which pairwise interactions drive contagion [ doi.org/10.1098/rsif... ].
2/7
New preprint out! 📣
Great collaboration w/ @martonkarsai.bsky.social and @alainbarrat.bsky.social!
How do adaptive behaviors, triggered by risk perception, affect higher-order contagion processes?
What happens to the contagion dynamics and to the phase transition?
arxiv.org/abs/2601.05801
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Here's a short thread about the EATH model! 👇
bsky.app/profile/marc...
2/2
The EATH model for realistic hypergraphs generation is now out in Physical Review E! 📣
journals.aps.org/pre/abstract...
with @giuliacencetti.bsky.social and @alainbarrat.bsky.social
1/2
The conference is officially underway – here are some moments from the opening session.
Our work opens several perspectives, from the generation of synthetic realistic hypergraphs describing contexts where data collection is difficult to a deeper understanding of dynamical processes on temporal hypergraphs. 8/8
Finally, we illustrate the flexibility of the model, which can generate synthetic hypergraphs with tunable properties: as an example, we generate ”hybrid” temporal hypergraphs, which mix properties of different empirical datasets, and artificial hypergraphs with specifically tuned properties. 7/8
We also showcase the possibility to use the resulting synthetic data in simulations of higher-order contagion dynamics, comparing the outcome of such process on original and surrogate datasets. 6/8