Abstract
In recent years, network representation learning on complex information networks attracts more and more attention. Scholars usually use matrix factorization or deep learning methods to learn network representation automatically. However, existing methods only preserve single feature of networks. How to effectively integrate multiple features of network is a challenge. To tackle this challenge, we propose an unsupervised learning algorithm named Multi-View Learning of Network Embedding. The algorithm preserves multiple features that including vertex attribute, network global and local topology structure. Features are treated as network views. We use a variant of convolutional neural networks to learn features from these views. The algorithm maximizes the correlation between different views by canonical correlation analysis, and learns the embedding that preserve multiple features of networks. Comprehensive experiments are conducted on five real networks. We demonstrate that our method can better preserve multiple features and outperform baseline algorithms in community detection, network reconstruction and visualization.
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
Zhao, D., Wang, L., Li, S., Wang, Z., et al.: Immunization of epidemics in multiplex networks. PLoS ONE 9(11), e112018 (2014)
Cozzo, E., Banos, R.A., Meloni, S., et al.: Contact-based social contagion in multiplex networks. Phys. Rev. E 88(5), 660–691 (2013)
Tan, S., Guan, Z., Cai, D., et al.: Mapping users across networks by manifold alignment on hypergraph. In: 28th AAAI Conference on Artificial Intelligence, vol. 1, pp. 159–165 (2014)
Maaten, L.v.d., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579–2605 (2008)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234 (2016)
Ou, M., Cui, P., Pei, J., Zhang, Z., et al.: Asymmetric transitivity preserving graph embedding. In: 22nd International Conference on Knowledge Discovery and Data Mining, pp. 1105–1114 (2016)
Bhagat, S., Cormode, G., Muthukrishnan, S.: Node classification in social networks. In: Aggarwal, C. (ed.) Social Network Data Analytics, pp. 115–148. Springer, Boston (2011). https://doi.org/10.1007/978-1-4419-8462-3_5
Ahmed, A., Shervashidze, N., Narayanamurthy, S., et al.: Distributed large-scale natural graph factorization. In: 22nd International Conference on World Wide Web, pp. 37–48 (2013)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: Proceedings 24th International Conference on World Wide Web, pp. 1067–1077. ACM (2015)
Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: 22nd International Conference on Knowledge Discovery and Data Mining, pp. 1105–1114 (2016)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016)
Mikolov, T., Chen, K., Corrado, G., et al.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: 33rd International Conference on Machine Learning, vol. 48, pp. 2014–2023 (2016)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16(12), 2639–2664 (2004)
Acknowledgment
This work is supported by the National Natural Science Foundation of China (Grant No. 61170112), Beijing Natural Science Foundation (4172016), and the Scientific Research Project of Beijing Educational Committee (KM201710011006), and Key Lab of Information Network Security, Ministry of Public Security).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Han, Z., Zheng, C., Liu, D., Duan, D., Yang, W. (2019). Multi-View Learning of Network Embedding. In: Kojima, K., Sakamoto, M., Mineshima, K., Satoh, K. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2018. Lecture Notes in Computer Science(), vol 11717. Springer, Cham. https://doi.org/10.1007/978-3-030-31605-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-31605-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31604-4
Online ISBN: 978-3-030-31605-1
eBook Packages: Computer ScienceComputer Science (R0)