Abstract
Graph neural networks (GNNs) have been extensively explored due to semi-supervised learning on graphs, which uses few labels to complete tasks without employing costly labeling information. Related methods are dedicated to mitigating the over-smoothing phenomenon to generate reliable node representations. However, existing methods lack correct guidance for neighbors and links from graph characteristics to node representations, resulting in incorrect neighbor information aggregation and poor representation discriminability. In this paper, we introduce a novel encoding and decoding framework that correctly leverages structure guided by labels and uses features for self-supervision of representations to alleviates the over-smoothing phenomenon, dubbed as Iterative Encode-and-Decode Graph Neural Network (IEDGNN). First, we offer a central component reconstruction module to correct the category centers of node representations, lowering the likelihood of aggregating neighbor information across categories. Then, we propose a feature self-reconstruction module that enables node representations to contain valid original attributes, making representations more informative in downstream classification tasks. We also theoretically analyze the impact of different encoder-decoder combinations on representation generation in our design. Extensive experiments demonstrate that our IEDGNN outperforms the state-of-the-art models on eight graph benchmark datasets with three label ratios.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Abu-El-Haija, S., Kapoor, A., Perozzi, B., Lee, J.: N-GCN: multi-scale graph convolution for semi-supervised node classification. In: Uncertainty in Artificial Intelligence, pp. 841–851. PMLR (2020)
Bo, D., Wang, X., Shi, C., Shen, H.: Beyond low-frequency information in graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 3950–3957 (2021)
Chien, E., Peng, J., Li, P., Milenkovic, O.: Adaptive universal generalized PageRank graph neural network. arXiv preprint arXiv:2006.07988 (2020)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Feng, J., Chen, Y., Li, F., Sarkar, A., Zhang, M.: How powerful are K-hop message passing graph neural networks. arXiv preprint arXiv:2205.13328 (2022)
Hou, Z., Liu, X., Dong, Y., Wang, C., Tang, J., et al.: GraphMAE: self-supervised masked graph autoencoders. arXiv preprint arXiv:2205.10803 (2022)
Jin, M., Chang, H., Zhu, W., Sojoudi, S.: Power up! Robust graph convolutional network against evasion attacks based on graph powering. arXiv e-prints, arXiv-1905 (2019)
Kim, D., Oh, A.: How to find your friendly neighborhood: graph attention design with self-supervision. arXiv preprint arXiv:2204.04879 (2022)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)
Klicpera, J., Bojchevski, A., Günnemann, S.: Predict then propagate: graph neural networks meet personalized PageRank. arXiv preprint arXiv:1810.05997 (2018)
Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Liu, Y., et al.: Deep graph clustering via dual correlation reduction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 7603–7611 (2022)
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)
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93 (2008)
Shchur, O., Mumme, M., Bojchevski, A., Günnemann, S.: Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868 (2018)
Sun, K., Zhu, Z., Lin, Z.: AdaGCN: AdaBoosting graph convolutional networks into deep models. In: International Conference on Learning Representations (2021)
Tu, W., Zhou, S., Liu, Y., Liu, X.: Siamese attribute-missing graph auto-encoder. arXiv preprint arXiv:2112.04842 (2021)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103 (2008)
Wang, Y., Wang, W., Liang, Y., Cai, Y., Hooi, B.: MixUp for node and graph classification. In: Proceedings of the Web Conference 2021, pp. 3663–3674 (2021)
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, Q., Zhao, W., Li, Z., Wipf, D., Yan, J.: NodeFormer: a scalable graph structure learning transformer for node classification. In: Advances in Neural Information Processing Systems (2022)
Xu, B., Shen, H., Cao, Q., Qiu, Y., Cheng, X.: Graph wavelet neural network. arXiv preprint arXiv:1904.07785 (2019)
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.I., Jegelka, S.: Representation learning on graphs with jumping knowledge networks. In: International Conference on Machine Learning, pp. 5453–5462. PMLR (2018)
Yang, X., Hu, X., Zhou, S., Liu, X., Zhu, E.: Interpolation-based contrastive learning for few-label semi-supervised learning. IEEE Trans. Neural Netw. Learn. Syst. (2022)
Zhang, S., Liu, Y., Sun, Y., Shah, N.: Graph-less neural networks: teaching old MLPs new tricks via distillation. arXiv preprint arXiv:2110.08727 (2021)
Acknowledgements
This work is supported by National Key R &D Program of China under Grant No. 2022ZD0209103.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Song, L., Wang, S., Zhou, S., Zhu, E. (2023). Iterative Encode-and-Decode Graph Neural Network. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-46674-8_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46673-1
Online ISBN: 978-3-031-46674-8
eBook Packages: Computer ScienceComputer Science (R0)