Identifying Asymptomatic Nodes in Network Epidemics using Graph Neural Networks

• URL: http://arxiv.org/abs/2510.02568v1
• Date: Thu, 02 Oct 2025 21:18:18 GMT
• Title: Identifying Asymptomatic Nodes in Network Epidemics using Graph Neural Networks
• Authors: Conrado Catarcione Pinto, Amanda Camacho Novaes de Oliveira, Rodrigo Sapienza Luna, Daniel Ratton Figueiredo,
• Abstract summary: This work tackles the problem of identifying asymptomatic individuals considering a classic SI (Susceptible-Infected) network epidemic model.<n>A Graph Neural Network (GNN) model with supervised learning is adopted where a set of node features are built from the network with observed infected nodes.<n>Results indicate that the proposed methodology is robust across different scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Infected individuals in some epidemics can remain asymptomatic while still carrying and transmitting the infection. These individuals contribute to the spread of the epidemic and pose a significant challenge to public health policies. Identifying asymptomatic individuals is critical for measuring and controlling an epidemic, but periodic and widespread testing of healthy individuals is often too costly. This work tackles the problem of identifying asymptomatic individuals considering a classic SI (Susceptible-Infected) network epidemic model where a fraction of the infected nodes are not observed as infected (i.e., their observed state is identical to susceptible nodes). In order to classify healthy nodes as asymptomatic or susceptible, a Graph Neural Network (GNN) model with supervised learning is adopted where a set of node features are built from the network with observed infected nodes. The approach is evaluated across different network models, network sizes, and fraction of observed infections. Results indicate that the proposed methodology is robust across different scenarios, accurately identifying asymptomatic nodes while also generalizing to different network sizes and fraction of observed infections.

Related papers

• Heuristic algorithms for the stochastic critical node detection problem [0.0]
  Given a network, the critical node detection problem finds a subset of nodes whose removal disrupts the network connectivity.<n>In this paper, we consider a version of the critical node detection problem, where the existence of edges is given by certain probabilities.<n>We proposes and learning-based methods for the problem and compare them with existing algorithms.
  arXiv  Detail & Related papers  (2025-12-01T10:18:24Z)
• Learning hidden cascades via classification [49.40566691717171]
  intermediate indicators such as symptoms of infection are observable.<n>We propose a partial observability-aware Machine Learning framework to learn the characteristics of the spreading model.<n>We validate the method on synthetic networks and extend the study to a real-world insider trading network.
  arXiv  Detail & Related papers  (2025-05-16T13:23:52Z)
• Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic Forecasting [46.63739322178277]
  Recent studies have demonstrated the strong potential of of-temporal neural networks (STGNNs) in extracting heterogeneous-temporal epidemic patterns. HeatGNN learns epidemiology-informed locations embedding different locations that reflect their own transmission mechanisms over time. HeatGNN outperforms various strong baselines of HeatHeat on different sizes of Heat.
  arXiv  Detail & Related papers  (2024-11-26T12:29:45Z)
• Multitask Active Learning for Graph Anomaly Detection [48.690169078479116]
  We propose a novel MultItask acTIve Graph Anomaly deTEction framework, namely MITIGATE. By coupling node classification tasks, MITIGATE obtains the capability to detect out-of-distribution nodes without known anomalies. Empirical studies on four datasets demonstrate that MITIGATE significantly outperforms the state-of-the-art methods for anomaly detection.
  arXiv  Detail & Related papers  (2024-01-24T03:43:45Z)
• Network-based Control of Epidemic via Flattening the Infection Curve: High-Clustered vs. Low-Clustered Social Networks [5.768625063623631]
  Clustered networks are, in general, easier to flatten the infection curve. Distance-based centrality measures are better choices for targeting individuals for isolation/vaccination.
  arXiv  Detail & Related papers  (2023-03-16T09:37:21Z)
• Epidemic inference through generative neural networks [0.0]
  We present a new generative neural networks framework that can sample the most probable infection cascades compatible with observations. The framework can infer the parameters governing the spreading of infections.
  arXiv  Detail & Related papers  (2021-11-05T10:40:10Z)
• Estimating the State of Epidemics Spreading with Graph Neural Networks [41.93923100501976]
  algorithmic solutions can be used to infer the state of the whole population from a limited amount of measures. We analyze the capability of deep neural networks to solve this challenging task.
  arXiv  Detail & Related papers  (2021-05-10T13:54:13Z)
• Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization [59.5104563755095]
  We introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding.
  arXiv  Detail & Related papers  (2020-09-27T12:30:30Z)
• Finding Patient Zero: Learning Contagion Source with Graph Neural Networks [67.3415507211942]
  Locating the source of an epidemic can provide critical insights into the infection's transmission course. Existing methods use graph-theoretic measures and expensive message-passing algorithms. We revisit this problem using graph neural networks (GNNs) to learn P0.
  arXiv  Detail & Related papers  (2020-06-21T21:12:44Z)
• Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots [18.227721607607183]
  Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19. We introduce a temporal point process modeling framework that specifically represents visits to the sites where individuals get in contact and infect each other. Under our model, the number of infections caused by an infectious individual naturally emerges to be overdispersed.
  arXiv  Detail & Related papers  (2020-04-15T17:18:32Z)
• 1-D Convlutional Neural Networks for the Analysis of Pupil Size Variations in Scotopic Conditions [79.71065005161566]
  1-D convolutional neural network models are trained for classification of short-range sequences. Model provides prediction with high average accuracy on a hold out test set.
  arXiv  Detail & Related papers  (2020-02-06T17:25:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.