Marco Mancastroppa

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

We first show that the EATH model can generate surrogate hypergraphs of several empirical datasets of face-to-face interactions, mimicking temporal and topological properties at the node and hyperedge level. 5/8

We present a new model, the Emerging Activity Temporal Hypergraph (EATH), which can create synthetic time-varying hypergraphs. Each node has an independent activity dynamics, the system activity emerges from it, with temporal group interactions resulting from activity and memory mechanisms. 4/8

Preserving friendships in school contacts: An algorithm to construct synthetic temporal networks for epidemic modelling Author summary Face-to-face contacts occur between individuals throughout day-to-day activities. These contacts form a network of opportunities for the spread of diseases, such as COVID-19 or influenz...

The corresponding datasets are often incomplete and/or limited in size and duration, and surrogate time-varying hypergraphs constitute interesting substitutions, especially to understand dynamical processes. [ journals.plos.org/ploscompbiol... ] 3/8

The temporal dynamics of group interactions in higher-order social networks - Nature Communications The structure and dynamics of many social systems where human interactions involve communities can be described by higher-order networks. The authors propose a hypergraph-based model that describes ho...

Time-varying group interactions constitute the building blocks of many complex systems. The framework of temporal hypergraphs makes it possible to represent them by taking into account the higher-order and temporal nature of the interactions. [ www.nature.com/articles/s41... ] 2/8

Emerging Activity Temporal Hypergraph (EATH), a model for generating realistic time-varying hypergraphs Time-varying group interactions constitute the building blocks of many complex systems. The framework of temporal hypergraphs makes it possible to represent them by taking into account the higher-orde...

New preprint out! 📣

“Emerging Activity Temporal Hypergraph (EATH), a model for generating realistic time-varying hypergraphs” with @giuliacencetti.bsky.social and @alainbarrat.bsky.social

arxiv.org/abs/2507.01124

How can we generate realistic time-varying hypergraphs?

1/8 🧵⬇️

Delighted to announce the #CCS25 Scientific Dissemination Event!
🌐Free public event in italian called "La complessità è semplice?"
📅 Wednesday, September 3rd, 20:30
📍Teatro dei Rinnovati in the Palazzo Pubblico of Siena
🔗 ccs25.cssociety.org/public-disse...

@xgi.bsky.social in the wild 👀

Cosimo giving a compelling overview of his pairwise and higher-order network comparison measures

@beyondtheedge.network student Cosimo Agostinelli presenting higher-order dissimilarity measures as a way to compare temporal snapshots, empirical data to synthetic null models, etc.

Very nice higher-order generative model to try and reproduce the empirical dynamics

The hypercoreness ranking correlation between two timestamps is strongly dependent on the timescale (negative correlations for long enough time gap)

@marco-mancastroppa.bsky.social talking about temporal evolution from the lens of "hypercores" --- a higher-order extension of the k-core measure for pairwise networks

YouTube video by fisica di tutti i giorni Interview with Nicole Yunger Halpern

Towards StatPhys2025: interview with
@nicoleyh11.bsky.social
(University of Maryland) by Franco Bagnoli (Dept. Physics and Astronomy, University of Florence & Caffè-Scienza).

youtu.be/oyIJTOGSmZE

YouTube video by fisica di tutti i giorni Interview with Lenka Zdeborova

Towards StatPhys2025: interview with @zdeborova.bsky.social (EPFL) by Franco Bagnoli (Dept. Physics and Astronomy, University of Florence & Caffè-Scienza).

youtu.be/hplPGzFSRyo

Our results highlight the advantages of using higher-order dissimilarity measures over traditional pairwise representations in capturing the full structural complexity of the systems considered. 5/5

We illustrate the effectiveness of these metrics through clustering experiments on synthetic and empirical higher-order networks, showing their ability to correctly group hypergraphs generated by different models and to distinguish real-world systems coming from different contexts. 4/5

Here we introduce two novel measures, Hyper NetSimile and Hyperedge Portrait Divergence, specifically designed for comparing hypergraphs, that take explicitly into account the properties of multi-node interactions, using complementary approaches. 3/5

What Are Higher-Order Networks? | SIAM Review Network-based modeling of complex systems and data using the language of graphs has become an essential topic across a range of different disciplines. Arguably, this graph-based perspective derives it...

Networks with higher-order interactions have emerged as a powerful tool to model complex systems. Comparing higher-order systems remains a challenge, since similarity measures designed for pairwise networks fail to capture salient features of hypergraphs. [ epubs.siam.org/doi/10.1137/... ] 2/5

Higher-order dissimilarity measures for hypergraph comparison In recent years, networks with higher-order interactions have emerged as a powerful tool to model complex systems. Comparing these higher-order systems remains however a challenge. Traditional similar...

Now out our new preprint “Higher-order dissimilarity measures for hypergraph comparison”!

www.arxiv.org/abs/2503.16959

With Cosimo Agostinelli and @alainbarrat.bsky.social

We extend to hypergraphs similarity measures defined previously only on pairwise networks.
🧵⬇️ 1/5

www.cnrs.fr/fr/presse/le...

YouTube video by fisica di tutti i giorni Intervista a Ginestra Bianconi

Verso StatPhys2025: intervista a @gin-bianconi.bsky.social (Queen Mary University of London) di Franco Bagnoli (Dip. Fisica e Astronomia, UNIFI & Caffè-Scienza).

www.youtube.com/watch?v=VZ_b...

The Complex Systems Society has taken a stance! Read about the Manifesto on the Publishing and Evaluation Systems at:
cssociety.org/about/manife...
#CSSManifesto

Now out the Complex Systems Society manifesto about the publishing and evaluation systems. Check it out!

cssociety.org/about/manife...

New work with @alainbarrat.bsky.social!
How to generate surrogate temporal networks to replace real ones:
arxiv.org/abs/2411.05477
Surrogates will have:
- no temporal length limit
- local dynamics similar to the real one
- mesoscale features like clustering and modularity
- long-term memory effects