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Although powerful graph neural networks (GNNs) have boosted numerous real-world applications, the potential privacy risk is still underexplored.
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Zhao, X., Zhang, W., Xiao, X., and Lim, B · 2021
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Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Link prediction based on graph neural networks
Zhang, M. and Chen, Y · 2018
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Graph neural networks for social recommendation
Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., and Yin, D · 2019
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Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement
Kool, W., Van Hoof, H., and Welling, M · 2019
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Similarity of neural network representations revisited
Kornblith, S., Norouzi, M., Lee, H., and Hinton, G · 2019
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Reparameterizable subset sampling via continuous relaxations
Xie, S. and Ermon, S · 2019
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Adversarial neural network inversion via auxiliary knowledge alignment
Yang, Z., Chang, E.-C., and Liang, Z · 2019
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Neural bellman-ford networks: A general graph neural network framework for link prediction
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Bietti, A., Wei, C.-Y., Dudik, M., Langford, J., and Wu, S · 2022
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Understanding and improving graph injection attack by promoting unnoticeability
Chen, Y., Yang, H., Zhang, Y., Ma, K., Liu, T., Han, B., and Cheng, J · 2022
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Label-only model inversion attacks via boundary repulsion
Kahla, M., Chen, S., Just, H. A., and Jia, R · 2022
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Interpretable and generalizable graph learning via stochastic attention mechanism
Miao, S., Liu, M., and Li, P · 2022
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Bilateral dependency optimization: Defending against model-inversion attacks
Peng, X., Liu, F., Zhang, J., Lan, L., Ye, J., Liu, T., and Han, B · 2022
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Model stealing attacks against inductive graph neural networks
Shen, Y., He, X., Han, Y., and Zhang, Y · 2022
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Plug and play attacks: Towards robust and flexible model inversion attacks
Struppek, L., Hintersdorf, D., Correia, A. D. A., Adler, A., and Kersting, K · 2022
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Understanding rare spurious correlations in neural network
Yang, Y.-Y., Chou, C.-N., and Chaudhuri, K · 2022
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Bringing your own view: Graph contrastive learning without prefabricated data augmentations
You, Y., Chen, T., Wang, Z., and Shen, Y · 2022
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Learning from counterfactual links for link prediction
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Combating exacerbated heterogeneity for robust models in federated learning
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