Abstract
Graphs evolved as very effective representations of different types of data including social networks, biological data or textual documents. In the past years, significant efforts have been devoted to methods that learn vector representations of nodes or of entire graphs. But edges, representing interactions between nodes, have attracted less attention. Surprisingly, there are only a few studies that focus on generating edge representations or deal with edge-related tasks such as the problem of edge classification. In this paper, we propose a new model (in the form of an auto-encoder) to learn edge embeddings in (un)directed graphs. The encoder corresponds to a graph neural network followed by an aggregation function, while a multi-layer perceptron serves as our decoder. We empirically evaluate our approach in two different tasks, namely edge classification and link prediction. In the first task, the proposed model outperforms the baselines, while in the second task, it achieves results that are comparable to the state-of-the-art.
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References
Abu-El-Haija, S., Perozzi, B., Al-Rfou, R.: Learning edge representations via low-rank asymmetric projections. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1787–1796 (2017)
Aggarwal, C., He, G., Zhao, P.: Edge classification in networks. In: Proceedings of the 32nd IEEE International Conference on Data Engineering, pp. 1038–1049 (2016)
Battaglia, P., Pascanu, R., Lai, M., Rezende, D.J.: Interaction networks for learning about objects, relations and physics. In: Advances in Neural Information Processing Systems, pp. 4502–4510 (2016)
Cai, H., Zheng, V.W., Chang, K.C.C.: A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans. Knowl. Data Eng. 30(9), 1616–1637 (2018)
Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings of the 24th International Conference on Information and Knowledge Management, pp. 891–900 (2015)
Chakrabarti, D., Faloutsos, C.: Graph mining: laws, generators, and algorithms. ACM Comput. Surv. 38(1), 2–es (2006)
Errica, F., Podda, M., Bacciu, D., Micheli, A.: A fair comparison of graph neural networks for graph classification. In: 8th International Conference on Learning Representations (2020)
Gao, Z., Fu, G., Ouyang, C., Tsutsui, S., Liu, X., Yang, J., Gessner, C., Foote, B., Wild, D., Ding, Y., Yu, Q.: edge2vec: representation learning using edge semantics for biomedical knowledge discovery. BMC Bioinform. 20(1), 306 (2019)
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: Proceedings of the 34th International Conference on Machine Learning, pp. 1263–1272 (2017)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp. 1024–1034 (2017)
Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprinthttps://arxiv.org/abs/1611.07308 (2016)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations (2017)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
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 (2012)
Nikolentzos, G., Tixier, A., Vazirgiannis, M.: Message passing attention networks for document understanding. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence, pp. 8544–8551 (2020)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Salha, G., Hennequin, R., Vazirgiannis, M.: Keep it simple: graph autoencoders without graph convolutional networks. arXiv preprinthttps://arxiv.org/abs/1910.00942 (2019)
Salha, G., Limnios, S., Hennequin, R., Tran, V.A., Vazirgiannis, M.: Gravity-inspired graph autoencoders for directed link prediction. In: Proceedings of the 28th International Conference on Information and Knowledge Management, pp. 589–598 (2019)
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93 (2008)
Song, C., Lin, Q., Ling, G., Zhang, Z., Chen, H., Liao, J., Chen, C.: LoCEC: local community-based edge classification in large online social networks. In: Proceedings of the 36th International Conference on Data Engineering, pp. 1689–1700 (2020)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077 (2015)
Tsitsulin, A., Mottin, D., Karras, P., Müller, E.: Verse: Versatile graph embeddings from similarity measures. In: Proceedings of the 2018 World Wide Web Conference, pp. 539–548 (2018)
Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 203–209 (2017)
Zhou, Y., Wu, S., Jiang, C., Zhang, Z., Dou, D., Jin, R., Wang, P.: Density-adaptive local edge representation learning with generative adversarial network multi-label edge classification. In: Proceedings of the 2018 IEEE International Conference on Data Mining, pp. 1464–1469 (2018)
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Rennard, V., Nikolentzos, G., Vazirgiannis, M. (2021). Graph Auto-Encoders for Learning Edge Representations. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-65351-4_10
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