Abstract
Graph embedding aims to embed the information of graph data into low-dimensional representation space. Prior methods generally suffer from an imbalance of preserving structural information and node features due to their pre-defined inductive biases, leading to unsatisfactory generalization performance. In order to preserve the maximal information, graph contrastive learning (GCL) has become a prominent technique for learning discriminative embeddings. However, in contrast with graph-level embeddings, existing GCL methods generally learn less discriminative node embeddings in a self-supervised way. In this paper, we ascribe above problem to two challenges: 1) graph data augmentations, which are designed for generating contrastive representations, hurt the original semantic information for nodes. 2) the nodes within the same cluster are selected as negative samples. To alleviate these challenges, we propose Contrastive Variational Graph Auto-Encoder (CVGAE). Specifically, we first propose a distribution-dependent regularization to guide the paralleled encoders to generate contrastive representations following similar distributions. Then, we utilize truncated triplet loss, which only selects top-k nodes as negative samples, to avoid over-separate nodes affiliated to the same cluster. Experiments on several real-world datasets show that our model CVGAE advanced performance over all baselines in link prediction, node clustering tasks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aggarwal, C.C., Wang, H.: A survey of clustering algorithms for graph data. In: Aggarwal, C.C., Wang, H. (eds.) Managing and Mining Graph Data, pp. 275–301. Springer, Boston (2010). https://doi.org/10.1007/978-1-4419-6045-0_9
Ahmed, M., Seraj, R., Islam, S.M.S.: The k-means algorithm: a comprehensive survey and performance evaluation. Electronics 9(8), 1295 (2020)
Ahn, S.J., Kim, M.: Variational graph normalized autoencoders. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management, pp. 2827–2831 (2021)
Cabanes, C., et al.: The cora dataset: validation and diagnostics of in-situ ocean temperature and salinity measurements. Ocean Sci. 9(1), 1–18 (2013)
Cai, T.T., Frankle, J., Schwab, D.J., Morcos, A.S.: Are all negatives created equal in contrastive instance discrimination? arXiv preprint arXiv:2010.06682 (2020)
Caragea, C., et al.: CiteSeer x: a scholarly big dataset. In: de Rijke, M., et al. (eds.) ECIR 2014. LNCS, vol. 8416, pp. 311–322. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06028-6_26
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)
Chuang, C.Y., Robinson, J., Lin, Y.C., Torralba, A., Jegelka, S.: Debiased contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 8765–8775 (2020)
Falagas, M.E., Pitsouni, E.I., Malietzis, G.A., Pappas, G.: Comparison of pubmed, scopus, web of science, and google scholar: strengths and weaknesses. FASEB J. 22(2), 338–342 (2008)
Fuglede, B., Topsoe, F.: Jensen-shannon divergence and hilbert space embedding. In: International Symposium on Information Theory, 2004 (ISIT 2004), Proceedings, p. 31. IEEE (2004)
Hassani, K., Khasahmadi, A.H.: Contrastive multi-view representation learning on graphs. In: International Conference on Machine Learning, pp. 4116–4126. PMLR (2020)
Hershey, J.R., Olsen, P.A.: Approximating the kullback leibler divergence between gaussian mixture models. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), vol. 4, pp. IV–317. IEEE (2007)
Hjelm, R.D., et al.: Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670 (2018)
Hou, Z., et al.: Graphmae: self-supervised masked graph autoencoders. arXiv preprint arXiv:2205.10803 (2022)
Jaiswal, A., Babu, A.R., Zadeh, M.Z., Banerjee, D., Makedon, F.: A survey on contrastive self-supervised learning. Technologies 9(1), 2 (2020)
Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)
Latham, P.E., Roudi, Y.: Mutual information. Scholarpedia 4(1), 1658 (2009)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)
Mavromatis, C., Karypis, G.: Graph infoclust: leveraging cluster-level node information for unsupervised graph representation learning. arXiv preprint arXiv:2009.06946 (2020)
Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)
Pan, L., Shi, C., Dokmanić, I.: Neural link prediction with walk pooling. arXiv preprint arXiv:2110.04375 (2021)
Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., Zhang, C.: Adversarially regularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407 (2018)
Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 776–794. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_45
Tokui, S., et al.: Reparameterization trick for discrete variables. arXiv preprint arXiv:1611.01239 (2016)
Veličković, P., Fedus, W., Hamilton, W.L., Liò, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. arXiv preprint arXiv:1809.10341 (2018)
Wang, G., Wang, K., Wang, G., Torr, P.H., Lin, L.: Solving inefficiency of self-supervised representation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9505–9515 (2021)
Wang, H., Li, Y., Huang, Z., Dou, Y., Kong, L., Shao, J.: SNCSE: contrastive learning for unsupervised sentence embedding with soft negative samples. arXiv preprint arXiv:2201.05979 (2022)
Xia, J., Wu, L., Chen, J., Hu, B., Li, S.Z.: Simgrace: a simple framework for graph contrastive learning without data augmentation. In: Proceedings of the ACM Web Conference 2022, pp. 1070–1079 (2022)
Xu, M.: Understanding graph embedding methods and their applications. SIAM Rev. 63(4), 825–853 (2021)
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. Adv. Neural. Inf. Process. Syst. 33, 5812–5823 (2020)
Zhang, S., Tong, H., Xu, J., Maciejewski, R.: Graph convolutional networks: a comprehensive review. Comput. Soc. Netw. 6(1), 1–23 (2019). https://doi.org/10.1186/s40649-019-0069-y
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Graph contrastive learning with adaptive augmentation. In: Proceedings of the Web Conference 2021, pp. 2069–2080 (2021)
Acknowledgments
This research was partially supported by grants from the National Natural Science Foundation of China (No. 61877051). We acknowledge all the developers and researchers for developing useful tools that enable our experiments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zu, S., Wang, C., Liu, Y., Shen, J., Li, L. (2024). Contrastive Learning Augmented Graph Auto-Encoder. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1965. Springer, Singapore. https://doi.org/10.1007/978-981-99-8145-8_22
Download citation
DOI: https://doi.org/10.1007/978-981-99-8145-8_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8144-1
Online ISBN: 978-981-99-8145-8
eBook Packages: Computer ScienceComputer Science (R0)