Effective epidemic containment strategy in hypergraphs

Jhun, Bukyoung

doi:10.1103/physrevresearch.3.033282

Cited by 24 publications

(11 citation statements)

References 78 publications

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“…With the eigenvectors derived from the specially designed matrix, the hypergraph node classification task can be solved in two steps: 1) The eigenvectors are utilised as inputs by a clustering algorithm that separates nodes into distinct clusters; and 2) Unlabeled nodes in each cluster are assigned labels based on the most common labels in the cluster. Most deep-learning-based methods (Feng et al, 2019;Bai et al, 2021;Chien et al, 2022;Wang et al, 2023a;b) develop HyperGNNs through adjusting the designs of existing GNNs (Kipf & Welling, 2017;Bodnar et al, 2022;Atwood & Towsley, 2016;Gilmer et al, 2017;Xu et al, 2019;Chen et al, 2021;Yang et al, 2021) to classify nodes in an end-to-end paradigm.…”

Section: Related Workmentioning

confidence: 99%

“…Higher-order interactions involving more than two entities exist in various domains, such as co-authorships in social science (Han et al, 2009) and the spreading phenomena in epidemiology (Jhun, 2021). To model these complicated interactions, hypergraphs, an extension of traditional graphs, are widely employed (Bick et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…The success of graph neural networks (GNNs) (Wu et al, 2020) highlights the proficiency of neural networks in processing data with pairwise interactions. Inspired by this, many hypergraph neural networks (HyperGNNs) have been proposed and widely used in the hypergraph node classification task (Feng et al, 2019;Bai et al, 2021;Chien et al, 2022;Wang et al, 2023a;b;Duta et al, 2023). These models are typically based on designs specific to hypergraphs, such as operators for learning hyperedge features Chien et al, 2022) or hypergraph energy functions (Wang et al, 2023b).…”

Section: Introductionmentioning

confidence: 99%

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Graph Neural Networks for Node Classification

Tang

Liao

2022

Graph Neural Networks: Foundations, Frontiers, and Applications

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This work analyzes Graph Neural Networks, a generalization of Fully-Connected Deep Neural Nets on Graph structured data, when their width, that is the number of nodes in each fullyconnected layer is increasing to infinity. Infinite Width Neural Networks are connecting Deep Learning to Gaussian Processes and Kernels, both Machine Learning Frameworks with long traditions and extensive theoretical foundations. Gaussian Processes and Kernels have much less hyperparameters then Neural Networks and can be used for uncertainty estimation, making them more user friendly for applications. This works extends the increasing amount of research connecting Gaussian Processes and Kernels to Neural Networks. The Kernel and Gaussian Process closed forms are derived for a variety of architectures, namely the standard Graph Neural Network, the Graph Neural Network with Skip-Concatenate Connections and the Graph Attention Neural Network. All architectures are evaluated on a variety of datasets on the task of transductive Node Regression and Classification. Additionally, a Spectral Sparsification method known as Effective Resistance is used to improve runtime and memory requirements. Extending the setting to inductive graph learning tasks (Graph Regression/ Classification) is straightforward and is briefly discussed in 3.5.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Graph Neural Networks for Node Classification

Tang

Liao

2022

Graph Neural Networks: Foundations, Frontiers, and Applications

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“…On the other hand, infected individuals recover and become susceptible individuals. The above process happens in a well-mixed population, but the SIS model has also been investigated on graphs [ 21 , 22 ] and hypergraphs [ 23 , 24 ]. Studies on introducing new factors into the SIS epidemic model continued to appear, including the study on vaccination [ 25 ], heterogeneous contacts [ 26 ], competing mechanism on complex networks [ 27 , 28 ], immigrants arriving with the same infection [ 29 ], and the external source of infection [ 30 – 32 ].…”

Section: Introductionmentioning

confidence: 99%

A simple epidemic model for semi-closed community reveals the hidden outbreak risk in nursing homes, prisons, and residential universities

Wang

2022

Int. J. Dynam. Control

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We develop a general SIS model to study the epidemic transmission in such semi-closed communities. The community population is divided into susceptible and infected in terms of the infection state, and concerning the physical structure of the crowd, they are classified into mobile and fixed individuals. The mobile individuals can be inside or outside the community, while the fixed individuals can be only inside the community. There are fixed infection sources outside the community, measuring the epidemic severity in society. We attribute the spreading to two reasons: (i) clustered infection among the community population and (ii) the epidemic in society spreading to the community population. We discuss the model in two cases. In the first case, the epidemic spreads in society, such that reasons (i) and (ii) work together. The results show that concerning fixed individuals (e.g. the elderly in nursing homes), a more closed community always promotes the infection. In the second case, there is no epidemic spreading in society, such that only reason (i) works. The results show that restricting all individuals to the community produces equivalent consequences as allowing them going outside the community. We should evenly distribute individuals inside and outside to form isolation. A counterexample is residential universities implementing closed management, where only students are restricted to campus. The model shows such management may lead to severe epidemics, and to prevent the epidemic outbreaks, students should have free access to being on or off campus.

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“…Containing, mitigating, and preventing the spread of epidemics is a crucial goal in mathematical epidemiology, therefore, extensive research has been devoted to developing efficient vaccination strategies in complex networks [8,[17][18][19][20][21][22][23][24][25][26][27][28]. Effective vaccination strategies aim to vaccinate the optimal set of nodes in the network to minimize the damage caused by epidemic diseases such as total number of infections or epidemic mortality.…”

Section: Introductionmentioning

confidence: 99%