Abstract
Gaussian embedding in unsupervised graph representation learning aims to embed vertices into Gaussian distributions. Downstream tasks such as link prediction and node classification can be efficiently computed on Gaussian distributions. Existing Gaussian embedding methods depending on adjacent neighbors of vertexes leave out of consideration of indirect connectivity information carried by remote vertice, which is still a part of local semantic connectivity in a network. In this paper, we propose an unsupervised graph representation model GLP2Gauss to improve Gaussian embedding by coarsening graph as indirect local connectivity. First, we decompose the original graph into paths and subgraphs. Secondly, we present a path-based embedding strategy combined with the Gaussian embedding method to maintain better local relevance of embedded nodes. Experiments evaluated on benchmark datasets show that GLP2Gauss has competitive performance on node classification and link prediction for networks compared with off-the-shelf network representation models.
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
References
Mikolov, T., Chen, K., Corrado, G., Dean, G.: Efficient estimation of word representations in vector space. In: Proceedings of ICLR (2013)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of KDD, pp. 701–710 (2014)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of KDD, pp. 855–864 (2016)
Pan, S., Zhu, X., Zhang, C., Wang, Y.: Tri-party deep network representation. In: Proceedings of IJCAI, pp. 1895–1901 (2016)
Bojchevski, A., Günnemann, S.: Deep Gaussian embedding of attributed graphs: Unsupervised inductive, CoRR, abs/1707.03815 (2017)
Zhu, D., Cui, P., Wang, D., Zhu, W.: Deep variational network embedding in Wasserstein space. In: Proceedings of KDD, pp. 2827–2836 (2018)
Hamilton, W., Ying, R., Leskovec, J.: Representation learning on graphs: Methods and applications, CoRR, abs/1709.05584 (2017)
Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.: Network representation learning with rich text information. In: Proceedings of IJCAI, pp. 2111–2117 (2015)
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., Huang, F.: A tutorial on energy-based learning. Predict. Struct. Data, 1 (2006)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: Proceedings of NIPS, pp. 321–328 (2003)
Cai, H., Zheng, V.M., Chang, K.C.C.: A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans. Knowl. Data Eng. 30(9), 1616–1637 (2018)
Vilnis, L., McCallum, A.: Word representations via Gaussian embedding. In: Proceedings of ICLR (2014)
He, S., Liu, K., Ji, G., Zhao, J.: Learning to represent knowledge graphs with Gaussian embedding. In: Proceedings of CIKM, pp. 623–632 (2015)
Givens, C.R., Shortt, M.: A class of Wasserstein metrics for probability distributions. Michigan Math. J. 31(2), 231–240 (1984)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: Proceedings of WWW, pp. 1067–1077 (2015)
Gunn, S.R.: Support vector machines for classification, and regression. ISIS Tech. Rep. 14(1), 5–16 (1998)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of KDD, pp. 1225–1234 (2016)
Dai, H., et al.: Adversarial attack on graph structured data. In: Proceedings of ICML, pp. 1123–1132 (2018)
Dong, Y., Chawla, N., Swami, A.: metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of KDD, pp. 135–144 (2018)
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of KDD, pp. 974–983 (2018)
Veliakovia, P., Fedus, W., Hamilton, W.L., Lio, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. CoRR, abs/1809.10341 (2018)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. CoRR, abs/1609.02907 (2016)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. CoRR, abs/1710.10903 (2017)
Sohn, K.: Improved deep metric learning with multi-class n-pair loss objective. In: Proceedings of NIPS, pp. 1857–1865 (2016)
Acknowlegements
This work is supported by the National Key Research and Development Program of China (2017YFC0908401) and the National Natural Science Foundation of China (61972455).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zheng, C., Wu, P., Zhang, X. (2020). Improving Gaussian Embedding for Extracting Local Semantic Connectivity in Networks. In: Nah, Y., Kim, C., Kim, SY., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020 International Workshops. DASFAA 2020. Lecture Notes in Computer Science(), vol 12115. Springer, Cham. https://doi.org/10.1007/978-3-030-59413-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-59413-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59412-1
Online ISBN: 978-3-030-59413-8
eBook Packages: Computer ScienceComputer Science (R0)