Integrating graph and reinforcement learning for vaccination strategies in complex networks
Bibliographic record
Abstract
Pandemics like COVID-19 have a huge impact on human society and the global economy. Vaccines are effective in the fight against these pandemics but often in limited supplies, particularly in the early stages. Thus, it is imperative to distribute such crucial public goods efficiently. Identifying and vaccinating key spreaders (i.e., influential nodes) is an effective approach to break down the virus transmission network, thereby inhibiting the spread of the virus. Previous methods for identifying influential nodes in networks lack consistency in terms of effectiveness and precision. Their applicability also depends on the unique characteristics of each network. Furthermore, most of them rank nodes by their individual influence in the network without considering mutual effects among them. However, in many practical settings like vaccine distribution, the challenge is how to select a group of influential nodes. This task is more complex due to the interactions and collective influence of these nodes together. This paper introduces a new framework integrating Graph Neural Network (GNN) and Deep Reinforcement Learning (DRL) for vaccination distribution. This approach combines network structural learning with strategic decision-making. It aims to efficiently disrupt the network structure and stop disease spread through targeting and removing influential nodes. This method is particularly effective in complex environments, where traditional strategies might not be efficient or scalable. Its effectiveness is tested across various network types including both synthetic and real-world datasets, demonstrting a potential for real-world applications in fields like epidemiology and cybersecurity. This interdisciplinary approach shows the capabilities of deep learning in understanding and manipulating complex network systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".