Abstract
Graph representation learning is the encoding of graph nodes into a low-dimensional representation space, which can effectively improve graph information representation while reducing the information dimensionality. To overcome the heavy reliance on label information of previous graph representation learning, the graph contrastive learning method has received attention from researchers as a self-supervised learning method, but it introduces the problem of sample selection dependence. To address this issue, inspired by deep clustering methods and image contrastive learning methods, we propose a novel Siamese network method, namely Community-enhanced Contrastive Siamese networks for Graph Representation Learning (MEDC). Specifically, we employ a Siamese network architecture to contrast two augmented views of the original graph and guide the network training by minimizing the similarity of positive sample nodes and negative sample nodes. Meanwhile, to take full advantage of the potential community structure of graph, we add a deep clustering layer in the network architecture, and the perceived community structure information is used to guide the selection of positive and negative samples. To demonstrate the effectiveness of the proposed method, we conducted a series of comparative experiments on three real datasets to validate the performance of our method.
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References
Zhang, D., Yin, J., Zhu, X., Zhang, C.: Network representation learning: a survey. IEEE Transactions on Big Data, pp. 3–28 (2018). https://doi.org/10.1109/tbdata.2018.2850013
Ktena, S.I., et al.: Distance metric learning using graph convolutional networks: application to functional brain networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 469–477. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_54
Lin, H., Ma, J., Cheng, M., Yang, Z., Chen, L., Chen, G.: Rumor detection on twitter with claim-guided hierarchical graph attention networks. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 10035–10047 (2021)
Bastings, J., Titov, I., Aziz, W., Marcheggiani, D., Sima’an, K.: Graph convolutional encoders for syntax-aware neural machine translation. The Association for Computational Linguistics (2017)
Ma, J., Zhou, C., Cui, P., Yang, H., Zhu, W.: Learning disentangled representations for a recommendation. In: Advances in Neural Information Processing Systems 32 (2019)
Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.Y.: Network representation learning with rich text information. In: Proceedings of the 24th International Joint Conference on Artifcial lntelligence, pp. 2111–2117 (2015)
Oord, A.V.D., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)
Guo, X., Gao, L., Liu, X., Yin, J.: Improved deep embedded clustering with local structure preservation. In: IJCAI, pp. 1753–1759 (2017)
Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: International Conference on Machine Learning, pp. 478–487. PMLR (2016)
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)
Hassani, K., Khasahmadi, A.H.: Contrastive multi-view representation learning on graphs. In: International Conference on Machine Learning, pp. 4116–4126. PMLR (2020)
Jin, M., Zheng, Y., Li, Y.F., Gong, C., Zhou, C., Pan, S. Multi-scale contrastive Siamese networks for self-supervised graph representation learning. arXiv preprint arXiv:2105.05682 (2021)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2016)
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1735–1742 (2006)
Mueller, J., Thyagarajan, A., Doshi-Velez, F.: Siamese recurrent architectures for learning sentence similarity. In: Thirty-First AAAI Conference on Artificial Intelligence (2016)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. In: Advances in Neural Information Processing Systems 33, pp. 21271-21284 (2020)
Zhao, H., Yang, X., Wang, Z., Yang, E., Deng, C.: Graph debiased contrastive learning with joint representation clustering. In: IJCAI, pp. 3434–3440 (2021)
Peng, Z., Liu, H., Jia, Y., Hou, J.: Attention-driven graph clustering network. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 935–943 (2021)
Peng, Z., et al.: Graph representation learning via graphical mutual information maximization. In: Proceedings of The Web Conference 2020, pp. 259–270 (2020)
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Deep graph contrastive representation learning. In: ICML Workshop on Graph Representation Learning and Beyond (2020)
Shchur, O., Mumme, M., Bojchevski, A., Günnemann, S.: Pitfalls of graph neural network evaluation. Computing Research Repository, abs/1811.05868 (2018)
Ghamrawi, N., McCallum, A.: Collective multi-label classification. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 195–200 (2005)
Younis, O., Krunz, M., Ramasubramanian, S.: Node clustering in wireless sensor networks: recent developments and deployment challenges. IEEE Network 20(3), 20–25 (2006)
Rendón, E., Abundez, I., Arizmendi, A., Quiroz, E.M.: Internal versus external cluster validation indexes. Int. J. Comput. Commun. 5(1), 27-34 (2011)
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Li, Y., Wang, W., Ma, G., Zu, B. (2023). Community-Enhanced Contrastive Siamese Networks for Graph Representation Learning. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14117. Springer, Cham. https://doi.org/10.1007/978-3-031-40283-8_26
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