Abstract
Graph neural networks (GNNs) have achieved outstanding performance in semi-supervised learning tasks with partially labeled graph structured data. However, labeling graph data for training is a challenging task, and inaccurate labels may mislead the training process to erroneous GNN models for node classification. In this paper, we consider label poisoning attacks on training data, where the labels of input data are modified by an adversary before training, to understand to what extent the state-of-the-art GNN models are resistant/vulnerable to such attacks. Specifically, we propose a label poisoning attack framework for graph convolutional networks (GCNs), inspired by the equivalence between label propagation and decoupled GCNs that separate message passing from neural networks. Instead of attacking the entire GCN models, we propose to attack solely label propagation for message passing. It turns out that a gradient-based attack on label propagation is effective and efficient towards the misleading of GCN training. More remarkably, such label attack can be topology-agnostic in the sense that the labels to be attacked can be efficiently chosen without knowing graph structures. Extensive experimental results demonstrate the effectiveness of the proposed method against state-of-the-art GCN-like models.
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Notes
- 1.
Publicly available at https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/.
References
Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. 31(5), 833–852 (2018)
Dai, E., Aggarwal, C., Wang, S.: NRGNN: learning a label noise-resistant graph neural network on sparsely and noisily labeled graphs. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 227–236 (2021)
Dai, E., Jin, W., Liu, H., Wang, S.: Towards robust graph neural networks for noisy graphs with sparse labels. arXiv preprint arXiv:2201.00232 (2022)
Dong, H., et al.: On the equivalence of decoupled graph convolution network and label propagation. In: Proceedings of the Web Conference 2021, pp. 3651–3662 (2021)
Fan, W., et al.: Graph neural networks for social recommendation. In: The World Wide Web Conference, pp. 417–426 (2019)
Gao, J., Zhang, T., Xu, C.: Graph convolutional tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4649–4659 (2019)
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017)
Jin, W., Li, Y., Xu, H., Wang, Y., Tang, J.: Adversarial attacks and defenses on graphs: a review and empirical study. arXiv e-prints pp. arXiv-2003 (2020)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24–26 April 2017, Conference Track Proceedings (2017)
Klicpera, J., Bojchevski, A., Günnemann, S.: Predict then propagate: graph neural networks meet personalized pagerank. In: International Conference on Learning Rep resentations, ICLR, 2019 (2019)
Liang, X., Shen, X., Feng, J., Lin, L., Yan, S.: Semantic object parsing with graph LSTM. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 125–143. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_8
Liu, C.Y., Zhou, C., Wu, J., Hu, Y., Guo, L.: Social recommendation with an essential preference space. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Liu, X., Si, S., Zhu, X., Li, Y., Hsieh, C.J.: A unified framework for data poisoning attack to graph-based semi-supervised learning. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp. 9780–9790 (2019)
McCallum, A.K., Nigam, K., Rennie, J., Seymore, K.: Automating the construction of internet portals with machine learning. Inf. Retrieval 3(2), 127–163 (2000)
Muñoz-González, L., et al.: Towards poisoning of deep learning algorithms with back-gradient optimization. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp. 27–38 (2017)
Namata, G., London, B., Getoor, L., Huang, B., Edu, U.: Query-driven active surveying for collective classification. In: 10th International Workshop on Mining and Learning with Graphs, vol. 8, p. 1 (2012)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab (1999)
Parisot, S.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and alzheimer’s disease. Med. Image Anal. 48, 117–130 (2018)
Rieck, B., Bock, C., Borgwardt, K.: A persistent weisfeiler-lehman procedure for graph classification. In: International Conference on Machine Learning, pp. 5448–5458. PMLR (2019)
Rosenfeld, E., Winston, E., Ravikumar, P., Kolter, Z.: Certified robustness to label-flipping attacks via randomized smoothing. In: International Conference on Machine Learning, pp. 8230–8241. PMLR (2020)
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netwo. 20(1), 61–80 (2008)
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93–93 (2008)
Shchur, O., Mumme, M., Bojchevski, A., Günnemann, S.: Pitfalls of graph neural network evaluation. In: Relational Representation Learning Workshop, R2L (2018)
Sun, L., et al.: Adversarial attack and defense on graph data: a survey. arXiv preprint arXiv:1812.10528 (2018)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, 30 April–3 May 2018, Conference Track Proceedings. (2018)
Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)
Wang, X., Gupta, A.: Videos as space-time region graphs. In: Proceedings of the European conference on computer vision (ECCV), pp. 399–417 (2018)
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph cnn for learning on point clouds. ACM Trans. Graph. (TOG) 38(5), 1–12 (2019)
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: International Conference on Machine Learning, pp. 6861–6871. PMLR (2019)
Wu, J., et al.: Learning differential diagnosis of skin conditions with co-occurrence supervision using graph convolutional networks. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 335–344. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59713-9_33
Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 974–983 (2018)
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. Adv. Neural Inf. Process. Syst. 31 (2018)
Zhang, M., Hu, L., Shi, C., Wang, X.: Adversarial label-flipping attack and defense for graph neural networks. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 791–800. IEEE (2020)
Zhang, M., Chen, Y.: Link prediction based on graph neural networks. Adv. Neural Inf. Process. Syst. 31 (2018)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. Adv. Neural Inf. Process. Syst., 321–328 (2004)
Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation (2002)
Acknowledgment
GL thanks the Department of Computer Science, University of Liverpool for the support of the attendance to the conference. XH is supported by the EPSRC under project [EP/T026995/1].
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Liu, G., Huang, X., Yi, X. (2022). Adversarial Label Poisoning Attack on Graph Neural Networks via Label Propagation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13665. Springer, Cham. https://doi.org/10.1007/978-3-031-20065-6_14
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