Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks

Xie, Jiaqing; Ying, Rex

doi:10.1007/978-3-030-93736-2_19

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…• EpiGNN [26]: a recent proposed epidemic prediction model which applies graph neural network to extract and refine the combination of epidemic features and geography features, then predict the future new case through a deep neural networks.…”

Section: Baselinesmentioning

confidence: 99%

Game and Reference: Efficient Policy Making for Epidemic Prevention and Control

Zhiyi,

Bingkun

2024

Preprint

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Epidemic policy-making, as a special data-mining task, is proposed to predict the proper intensities of certain epidemic prevention and control policies based on the spatial-temporal data related to regional epidemics.Previous studies are currently constrained by two issues: First, existing methods are all strongly supervised by policy effect evaluation, since only a small proportion of factors in real-world policy-making are modeled, policies made by the existing models are then easily become extreme or unreasonable. Second, the subjectivity and the cognitive limitation of humans make historical policies not always optimal for the training of decision models. To this end, we present a novel Policy Combination Synthesis (PCS) model for epidemic policy-making. In particular, to prevent extreme decisions, we introduce adversarial learning between the model-made policies and the real policies to force the output policies to be more human-like. On the other hand, to minimize the impact of sub-optimal historical policies, we employ contrastive learning to let the model draw on experience from the best historical policies under similar scenarios. Both adversarial learning and contrastive learning are adaptive to the comprehensive effects of real policies, therefore ensuring that the model always learns useful information.Extensive experiments on real-world data show that policies made by the proposed model outperform the baseline models on both the epidemic containment effect and the economic impact, thereby proving the effectiveness of our work.

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Section: Baselinesmentioning

confidence: 99%

Game and Reference: Efficient Policy Making for Epidemic Prevention and Control

Zhiyi,

Bingkun

2024

Preprint

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Understanding Universal Adversarial Attack and Defense on Graph

Wang

Pan

et al. 2022

International Journal on Semantic Web and Information Systems

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Compared with traditional machine learning model, graph neural networks (GNNs) have distinct advantages in processing unstructured data. However, the vulnerability of GNNs cannot be ignored. Graph universal adversarial attack is a special type of attack on graph which can attack any targeted victim by flipping edges connected to anchor nodes. In this paper, we propose the forward-derivative-based graph universal adversarial attack (FDGUA). Firstly, we point out that one node as training data is sufficient to generate an effective continuous attack vector. Then we discretize the continuous attack vector based on forward derivative. FDGUA can achieve impressive attack performance that three anchor nodes can result in attack success rate higher than 80% for the dataset Cora. Moreover, we propose the first graph universal adversarial training (GUAT) to defend against universal adversarial attack. Experiments show that GUAT can effectively improve the robustness of the GNNs without degrading the accuracy of the model.

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Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks

Cited by 2 publications

References 15 publications

Game and Reference: Efficient Policy Making for Epidemic Prevention and Control

Game and Reference: Efficient Policy Making for Epidemic Prevention and Control

Understanding Universal Adversarial Attack and Defense on Graph

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