Here's a short thread about the EATH model! 👇

bsky.app/profile/marc...

2/2

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

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

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

@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

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

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