Information spreading dynamics in hypernetworks

Suo, Qi; Guo, Jun; Shen, Ai-Zhong

doi:10.1016/j.physa.2017.12.108

Cited by 38 publications

(20 citation statements)

References 37 publications

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“…We also showcased its use as reference models in investigating epidemic spreading and evolution of cooperation on hypergraphs. Models of dynamical processes on hypergraphs, such as the epidemic spreading [56][57][58]60], evolutionary dynamics [45,61], opinion dynamics [62][63][64], and synchronization [65][66][67][68], have been proposed. Deploying the hyper dK-series to studies of various models of dynamics is expected to better reveal how the dynamics depend on the specific structural properties of the given hypergraphs.…”

Section: Discussionmentioning

confidence: 99%

“…Each node is in either the susceptible state or the infectious state at any time t. Each infectious node recovers and becomes susceptible according to a Poisson process with rate δ. A fundamental assumption underlying the present model, which distinguishes it from other SIS models on hypergraphs [56][57][58], is that the contagion process is critical-mass dynamics, which generalizes a previous model [59]. Let ρ j denote the fraction of infectious nodes in hyperedge e j ∈ E. For each hyperedge e j , each susceptible node in e j becomes infected at rate λ j if and only if ρ j ≥ θ, where θ is a parameter.…”

Section: Datamentioning

confidence: 95%

See 1 more Smart Citation

Randomizing hypergraphs preserving degree correlation and local clustering

Nakajima¹,

Shudo²,

Masuda³

2021

Preprint

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Many complex systems involve direct interactions among more than two entities and can be represented by hypergraphs, in which hyperedges encode higher-order interactions among an arbitrary number of nodes. To analyze structures and dynamics of given hypergraphs, a solid practice is to compare them with those for randomized hypergraphs that preserve some specific properties of the original hypergraphs. In the present study, we propose a family of such reference models for hypergraphs, called the hyper dK-series, by extending the so-called dK-series for dyadic networks to the case of hypergraphs. The hyper dK-series preserves up to the individual node's degree, node's degree correlation, node's redundancy coefficient, and/or the hyperedge's size depending on the parameter values. We also apply the hyper dK-series to numerical simulations of epidemic spreading and evolutionary game dynamics on empirical hypergraphs.

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Section: Discussionmentioning

confidence: 99%

Section: Datamentioning

confidence: 95%

Randomizing hypergraphs preserving degree correlation and local clustering

Nakajima¹,

Shudo²,

Masuda³

2021

Preprint

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“…Suo et al [63] investigated a similar SIS model on hypergraphs in the context of rumor spreading on social media. They proposed two information diffusion models by considering how an individual might decide to share content on a social media platform, either to all the contacts or targeting a particular group.…”

Section: Related Work a Epidemic Models On Hypergraphsmentioning

confidence: 99%

Modeling and Evaluating Epidemic Control Strategies With High-Order Temporal Networks

et al. 2021

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Non-Pharmaceutical Interventions (NPIs) are essential measures that reduce and control a severe outbreak or a pandemic, especially in the absence of drug treatments. However, estimating and evaluating their impact on society remains challenging, considering the numerous and closely tied aspects to examine. This article proposes a fine-grain modeling methodology for NPIs, based on high-order relationships between people and environments, mimicking direct and indirect contagion pathways over time. After assessing the ability of each intervention in controlling an epidemic propagation, we devise a multi-objective optimization framework, which, based on the epidemiological data, calculates the NPI combination that should be implemented to minimize the spread of an epidemic as well as the damage due to the intervention. Each intervention is thus evaluated through an agent-based simulation, considering not only the reduction in the fraction of infected but also to what extent its application damages the daily life of the population. We run experiments on three data sets, and the results illustrate how the application of NPIs should be tailored to the specific epidemic situation. They further highlight the critical importance of correctly implementing personal protective (e.g., using face masks) and sanitization measures to slow down a pathogen spreading, especially in crowded places.

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“…As summarized in a very recent review paper [11,Sec. 7.1.2], there is some study of spreading process dynamics in hypergraphs [12][13][14][15], but no study on source localization.…”

Section: Introductionmentioning

confidence: 99%

Social Bubbles and Superspreaders: Source Identification for Contagion Processes on Hypertrees

Spencer

Varshney

2020

Preprint

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Previous work has shown that for contagion processes on extended star networks (trees with exactly one node of degree > 2), there is a simple, closed-form expression for a highly accurate approximation to the maximum likelihood infection source. Here, we generalize that result to a class of hypertrees which, although somewhat structurally analogous, provides a much richer representation space. In particular, this approach can be used to estimate patient zero sources, even when the infection has been propagated via large group gatherings rather than person-to-person spread, and when it is spreading through interrelated social bubbles with varying degrees of overlap. In contact tracing contexts, this estimator may be used to identify the source of a local outbreak, which can then be used for forward tracing or for further backward tracing (by similar or other means) to an upstream source.

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Information spreading dynamics in hypernetworks

Cited by 38 publications

References 37 publications

Randomizing hypergraphs preserving degree correlation and local clustering

Randomizing hypergraphs preserving degree correlation and local clustering

Modeling and Evaluating Epidemic Control Strategies With High-Order Temporal Networks

Social Bubbles and Superspreaders: Source Identification for Contagion Processes on Hypertrees

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