Abstract
Network embedding is a classical task which aims to project a network into a low-dimensional space. Currently, most existing embedding methods are unsupervised algorithms, which ignore the useful label information. In this paper, we propose TLINE, a semi-supervised extension of LINE algorithm. TLINE is a transductive network embedding method, which optimizes the loss function of LINE to preserve both local and global network structure information, and applies SVM to maximize the margin between the labeled nodes of different classes. By applying the edge-sampling and the negative sampling techniques in the optimizing process, the computational complexity of TLINE is reduced. Thus TLINE can handle the large-scale network. To evaluate the performance in node classification task, we test our methods on two real world network datasets, which are Citeseer and DBLP. The experimental result indicates that TLINE outperforms state-of-the-art baselines and is suitable for large-scale networks.
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
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: NIPS, vol. 14, pp. 585–591 (2001)
Chang, J., Blei, D.M.: Relational topic models for document networks. In: International Conference on Artificial Intelligence and Statistics, pp. 81–88 (2009)
Chen, M., Yang, Q., Tang, X.: Directed graph embedding. In: IJCAI, pp. 2707–2712 (2007)
Cox, T.F., Cox, M.A.: Multidimensional Scaling. CRC Press, Boca Raton (2000)
Jacob, Y., Denoyer, L., Gallinari, P.: Learning latent representations of nodes for classifying in heterogeneous social networks. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 373–382. ACM (2014)
Laurens, V.D.M., Hinton, G.: Viualizing data using t-SNE. J. Mach. Learn. Res. 9(2605), 2579–2605 (2008)
Le, T., Lauw, H.W.: Probabilistic latent document network embedding. In: IEEE International Conference on Data Mining (ICDM), pp. 270–279. IEEE (2014)
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)
Nallapati, R.M., Ahmed, A., Xing, E.P., Cohen, W.W.: Joint latent topic models for text and citations. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 542–550. ACM (2008)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)
Recht, B., Re, C., Wright, S., Niu, F.: Hogwild: a lock-free approach to parallelizing stochastic gradient descent. In: Advances in Neural Information Processing Systems, pp. 693–701 (2011)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Tang, J., Liu, J., Zhang, M., Mei, Q.: Visualizing large-scale and high-dimensional data. In: Proceedings of the 25th International Conference on World Wide Web, pp. 287–297. International World Wide Web Conferences Steering Committee (2016)
Tang, J., Lou, T., Kleinberg, J., Wu, S.: Transfer link prediction across heterogeneous social networks. ACM TOIS, 9(4), 1–42, Article 43 (2015). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.696.2188&rep=rep1&type=pdf
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. International World Wide Web Conferences Steering Committee (2015)
Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Tu, C., Zhang, W., Liu, Z., Sun, M.: Max-margin DeepWalk: discriminative learning of network representation. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI 2016), pp. 3889–3895 (2016)
Yang, Z., Cohen, W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. arXiv preprint arXiv:1603.08861 (2016)
Acknowledgments
This work is supported by 973 with Grant No. 2014CB340400, NSFC with Grant No. U1536201 and NSFC with Grant No. 61472013. And we also thank the three anonymous reviewers for their comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Zhang, X., Chen, W., Yan, H. (2016). TLINE: Scalable Transductive Network Embedding. In: Ma, S., et al. Information Retrieval Technology. AIRS 2016. Lecture Notes in Computer Science(), vol 9994. Springer, Cham. https://doi.org/10.1007/978-3-319-48051-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-48051-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48050-3
Online ISBN: 978-3-319-48051-0
eBook Packages: Computer ScienceComputer Science (R0)