Abstract
Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. Most GNNs use the message passing mechanism to obtain a distinguished feature representation. However, due to this message passing mechanism, most existing GNNs are inherently restricted by over-smoothing and poor robustness. Therefore, we propose a simple yet effective Network Embedding framework Without Neighborhood Aggregation (NE-WNA). Specifically, NE-WNA removes the neighborhood aggregation operation from the message passing mechanism. It only takes node features as input and then obtains node representations by a simple autoencoder. We also design an enhanced neighboring contrastive (ENContrast) loss to incorporate the graph structure into the node representations. In the representation space, the ENContrast encourages low-order neighbors to be closer to the target node than high-order neighbors. Experimental results show that NE-WNA enjoys high accuracy on the node classification task and high robustness against adversarial attacks.
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References
Waikhom, L., Patgiri, R.: Graph neural networks: methods, applications, and opportunities. ar**v preprint ar**v:2108.10733 (2021)
Feng, W., et al.: Graph random neural networks for semi-supervised learning on graphs. In: NIPS (2020)
Feng, W., et al.: GRAND+: scalable graph random neural networks. In: WWW, pp. 3248–3258 (2022)
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: ICLR (2019)
Wang, X., He, X., Wang, M., Feng, F., Chua, T.: Neural graph collaborative filtering. In: SIGIR, pp. 165–174 (2019)
Li, Q., Han, Z., Wu, X.: Deeper insights into graph convolutional networks for semi-supervised learning. In: AAAI, pp. 3538–3545 (2018)
Oono, K., Suzuki, T.: Graph neural networks exponentially lose expressive power for node classification. In: ICLR (2020)
Zhu, D., Zhang, Z., Cui, P., Zhu, W.: Robust graph convolutional networks against adversarial attacks. In: SIGKDD, pp. 1399–1407 (2019)
Zügner, D., Akbarnejad, A., Günnemann, S.: Adversarial attacks on neural networks for graph data. In: SIGKDD, pp. 2847–2856 (2018)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: ICLR (2018)
Klicpera, J., Bojchevski, A., Günnemann, S.: Predict then Propagate: graph neural networks meet personalized PageRank. In: ICLR (2019)
Wu, F., de Souza Jr., A.H., Zhang, T., Fifty, C., Yu, T., Weinberger, K.Q.: Simplifying graph convolutional networks. In: ICML, pp. 6861–6871 (2019)
Rossi, E., Frasca, F., Chamberlain, B., Eynard, D., Bronstein, M.M., Monti, F.: SIGN: scalable inception graph neural networks. ar**v preprint ar**v:2004.11198 (2020)
Zhu, H., Koniusz, P.: Simple spectral graph convolution. In: ICLR (2021)
Rong, Y., Huang, W., Xu, T., Huang, J.: DropEdge: towards deep graph convolutional networks on node classification. In: ICLR (2020)
Chen, M., Wei, Z., Huang, Z., Ding, B., Li, Y.: Simple and deep graph convolutional networks. In: ICML, pp. 1725–1735 (2020)
Wu, H., Wang, C., Tyshetskiy, Y., Docherty, A., Lu, K., Zhu, L.: Adversarial examples for graph data: deep insights into attack and defense. In: IJCAI, pp. 4816–4823 (2019)
Entezari, N., Al-Sayouri, S.A., Darvishzadeh, A., Papalexakis, E.E.: All you need is low (Rank): defending against adversarial attacks on graphs. In: WSDM, pp. 169–177 (2020)
**, W., Ma, Y., Liu, X., Tang, X., Wang, S., Tang, J.: Graph structure learning for robust graph neural networks. In: SIGKDD, pp. 66–74 (2020)
Wu, L., Lin, H., Gao, Z., Tan, C., Li, S.Z.: Self-supervised on graphs: contrastive, generative, or predictive. ar**v preprint ar**v:2105.07342 (2021)
Liu, Y., Pan, S., **, M., Zhou, C., **a, F., Yu, P.S.: Graph self-supervised learning: a survey. ar**v preprint ar**v:2103.00111 (2021)
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Deep graph contrastive representation learning. ar**v preprint ar**v:2006.04131 (2020)
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Graph contrastive learning with adaptive augmentation. In: WWW, pp. 2069–2080 (2021)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: SIGKDD, pp. 1225–1234 (2016)
Bo, D., Wang, X., Shi, C., Zhu, M., Lu, E., Cui, P.: Structural deep clustering network. In: WWW, pp. 1400–1410 (2020)
Hinton, G.E., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)
Shchur, O., Mumme, M., Bojchevski, A., Günnemann, S.: Pitfalls of graph neural network evaluation. ar** knowledge networks. In: ICML, pp. 5449–5458 (2018)
Zhu, Y., Xu, W., Zhang, J., Liu, Q., Wu, S., Wang, L.: Deep graph structure learning for robust representations: a survey. ar**v preprint ar**v:2103.03036 (2021)
Zügner, D., Günnemann, S.: Adversarial attacks on graph neural networks via meta learning. In: ICLR (2019)
Li, Y., **, W., Xu, H., Tang, J.: DeepRobust: a PyTorch library for adversarial attacks and defenses. ar**v preprint ar**v:2005.06149 (2020)
Van Der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 61972135), and the Natural Science Foundation of Heilongjiang Province in China (No. LH2020F043).
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Zhang, J., Yang, Y., Liu, Y., Han, M. (2023). NE-WNA: A Novel Network Embedding Framework Without Neighborhood Aggregation. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13714. Springer, Cham. https://doi.org/10.1007/978-3-031-26390-3_26
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