Hypergraph reconstruction from network data

Young, Jean-Gabriel; Petri, Giovanni; Peixoto, Tiago P.

doi:10.48550/arxiv.2008.04948

Cited by 2 publications

(3 citation statements)

References 51 publications

(80 reference statements)

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“…Section 5, using dyadic data to reconstruct the actual higher-order interactions is in general far from trivial [36] and it seems crucial to develop new methods to this aim.…”

Section: Discussionmentioning

confidence: 99%

“…In fact, data already encoded into graphs are intrinsically ill-suited for the task-since they have already been "projected" into pairwise relations (the links of the graph). Although recovering the hidden higher-order interactions from pairwise networks surely represent a challenging task, recent efforts have addressed this problem with a Bayesian approach [36]. Here, leveraging high-resolution proximity contact data provided by the SocioPatterns collaboration , we consider simplicial complexes representing interactions in four different social contexts: a workplace (InVS15 [37]), a conference (SFHH [38]), a hospital (LH10 [39]) and a high school (Thiers13 [40]).…”

Section: Simulations On Real-world Structuresmentioning

confidence: 99%

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Social contagion on higher-order structures

Barrat,

de Arruda,

Iacopini

et al. 2021

Preprint

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In this Chapter, we discuss the effects of higher-order structures on SISlike processes of social contagion. After a brief motivational introduction where we illustrate the standard SIS process on networks and the difference between simple and complex contagions, we introduce spreading processes on higher-order structures starting from the most general formulation on hypergraphs and then moving to several mean-field and heterogeneous mean-field approaches. The results highlight the rich phenomenology brought by taking into account higher-order contagion effects: both continuous and discontinuous transitions are observed, and critical mass effects emerge. We conclude with a short discussion on the theoretical results regarding the nature of the epidemic transition and the general need for data to validate these models.

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“…Section 5, using dyadic data to reconstruct the actual higher-order interactions is in general far from trivial [36] and it seems crucial to develop new methods to this aim.…”

Section: Discussionmentioning

confidence: 99%

Section: Simulations On Real-world Structuresmentioning

confidence: 99%

Social contagion on higher-order structures

Barrat,

de Arruda,

Iacopini

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…For instance, for some systems higher-order interactions might be difficult to observe, or only be recorded as a collection of pairwise data. To overcome this limitation, recent work has developed a bayesian framework to reconstruct higher-order connections from simple pairwise interactions following a principle of parsimony [23].…”

Section: Introductionmentioning

confidence: 99%

Detecting informative higher-order interactions in statistically validated hypergraphs

Musciotto¹,

Battiston²,

Mantegna³

2021

Preprint

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Recent empirical evidence has shown that in many real-world systems, successfully represented as networks, interactions are not limited to dyads, but often involve three or more agents at a time. These data are better described by hypergraphs, where hyperlinks encode higher-order interactions among a group of nodes. In spite of the large number of works on networks, highlighting informative hyperlinks in hypergraphs obtained from real world data is still an open problem. Here we propose an analytic approach to filter hypergraphs by identifying those hyperlinks that are over-expressed with respect to a random null hypothesis, and represent the most relevant higher-order connections. We apply our method to a class of synthetic benchmarks and to several datasets. For all cases, the method highlights hyperlinks that are more informative than those extracted with pairwise approaches. Our method provides a first way to obtain statistically validated hypergraphs, separating informative connections from redundant and noisy ones.

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Hypergraph reconstruction from network data

Cited by 2 publications

References 51 publications

Social contagion on higher-order structures

Social contagion on higher-order structures

Detecting informative higher-order interactions in statistically validated hypergraphs

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