Abstract
This paper studies the relationship prediction problem in multi-network scenarios, aiming to overcome the network sparsity challenge where the labeled data (connected node pairs) are much less than the unlabeled data (unconnected node pairs). The TAQIL framework is proposed by using transfer learning to get knowledge from the related source networks and then use active learning to query the labels of the most informative instances from the oracle in the target network. A new query function is also proposed in order to better use the parameters output by the transfer learning method. The alternate use of transfer learning and active learning allows adaptive transfer of knowledge across multiple networks to mitigate cold start and meantime improve the prediction accuracy with active queries in the target network. The experimental results on both non-network datasets and network datasets demonstrate the significant improvement in prediction accuracy compared with several benchmark methods and related state-of-art methods.
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
Similar content being viewed by others
References
Cao, B., Liu, N.N., Yang, Q.: Transfer learning for collective link prediction in multiple heterogenous domains. In: Proceedings of International Conference on Machine Learning, pp. 159–166. Citeseer (2010)
Chattopadhyay, R., Fan, W., Davidson, I., Panchanathan, S., Ye, J.: Joint transfer and batch-mode active learning. In: Proceedings of International Conference on Machine Learning, pp. 253–261 (2013)
Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Boosting for transfer learning. In: Proceedings of International Conference on Machine Learning, pp. 193–200. ACM (2007)
Dong, Y., Zhang, J., Tang, J., Chawla, N.V., Wang, B.: CoupledLP: link prediction in coupled networks. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–208. ACM (2015)
Getoor, L., Diehl, C.P.: Link mining: a survey. SIGKDD Explor. 7(2), 3–12 (2005)
Hasan, M.A., Zaki, M.J.: A survey of link prediction in social networks. In: Aggarwal, C. (ed.) Social Network Data Analytics, pp. 243–275. Springer, Boston (2011). https://doi.org/10.1007/978-1-4419-8462-3_9
Huang, S.J., Chen, S.: Transfer learning with active queries from source domain. In: Proceedings of IJCAI, pp. 1592–1598 (2016)
Kale, D., Ghazvininejad, M., Ramakrishna, A., He, J., Liu, Y.: Hierarchical active transfer learning. In: Proceedings of SIAM International Conference on Data Mining, pp. 514–522. SIAM (2015)
Kale, D., Liu, Y.: Accelerating active learning with transfer learning. In: Proceedings of IEEE International Conference on Data Mining, pp. 1085–1090. IEEE (2013)
Li, S., Xue, Y., Wang, Z., Zhou, G.: Active learning for cross-domain sentiment classification. In: IJCAI, pp. 2127–2133 (2013)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Saha, A., Rai, P., Daumé, H., Venkatasubramanian, S., DuVall, S.L.: Active supervised domain adaptation. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6913, pp. 97–112. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23808-6_7
Settles, B.: Active learning literature survey. Univ. Wisconsin-Madison, Madison, WI. Technical report, CS Technical report 1648 (2009)
Shi, X., Fan, W., Ren, J.: Actively transfer domain knowledge. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008. LNCS (LNAI), vol. 5212, pp. 342–357. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87481-2_23
Tang, J., Lou, T., Kleinberg, J., Wu, S.: Transfer learning to infer social ties across heterogeneous networks. ACM Trans. Inf. Syst. (TOIS) 34(2), 7 (2016)
Wang, X., Huang, T.K., Schneider, J.: Active transfer learning under model shift. In: Proceedings of IEEE International Conference on Machine Learning, pp. 1305–1313 (2014)
Yang, L., Hanneke, S., Carbonell, J.: A theory of transfer learning with applications to active learning. Mach. Learn. 90(2), 161–189 (2013)
Zhang, J., Kong, X., Philip, S.Y.: Predicting social links for new users across aligned heterogeneous social networks. In: Proceedings of IEEE International Conference on Data Mining, pp. 1289–1294. IEEE (2013)
Zhao, L., Pan, S.J., Yang, Q.: A unified framework of active transfer learning for cross-system recommendation. Artif. Intell. 245, 38–55 (2017)
Acknowledgments
This research was supported by the National Natural Science Foundation of China (No. 61571238 and No. 61603197).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, KJ., Zhang, K., Jiang, XL., Wang, Y. (2018). Transfer Learning with Active Queries for Relational Data Modeling Across Multiple Information Networks. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11303. Springer, Cham. https://doi.org/10.1007/978-3-030-04182-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-04182-3_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04181-6
Online ISBN: 978-3-030-04182-3
eBook Packages: Computer ScienceComputer Science (R0)