Abstract
In the past few years, various methods have been developed that attempt to embed graph nodes (e.g. users that interact through a social platform) onto low-dimensional vector spaces, exploiting the relationships (commonly displayed as edges) among them. The extracted vector representations of the graph nodes are then used to effectively solve machine learning tasks such as node classification or link prediction. These methods, however, focus on the static properties of the underlying networks, neglecting the temporal unfolding of those relationships. This affects the quality of representations, since the edges don’t encode the response times (i.e. speed) of the users’ (i.e. nodes) interactions. To overcome this limitation, we propose an unsupervised method that relies on temporal random walks unfolding at the same timescale as the evolution of the underlying dataset. We demonstrate its superiority against state-of-the-art techniques on the tasks of hidden link prediction and future link forecast. Moreover, by interpolating between the fully static and fully temporal setting, we show that the incorporation of topological information of past interactions can further increase our method efficiency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
However, many other processes can be represented as an ordered sequence.
References
Ahmed, A., Shervashidze, N., Narayanamurthy, S., Josifovski, V., Smola, A.J.: Distributed large-scale natural graph factorization. In: Proceedings of the 22nd International Conference on World Wide Web. WWW 2013, pp. 37–48. ACM, New York (2013)
Cai, H., Zheng, V.W., Chang, K.C.C.: A comprehensive survey of graph embedding: problems, techniques and applications. arXiv:1709.07604 [cs.AI] (2018)
Gehrke, J., Ginsparg, P., Kleinberg, J.: Overview of the 2003 KDD cup. SIGKDD Explor. Newsl. 5(2), 149–151 (2003)
Goyal, P., Kamra, N., He, X., Liu, Y.: DynGEM: deep embedding method for dynamic graphs (2017). http://www-scf.usc.edu/~nkamra/pdf/dyngem.pdf
Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM, New York (2016)
Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. arXiv:1605.09096 [cs.CL] (2016)
Hogg, T., Lerman, K.: Social dynamics of Digg. EPJ Data Sci. 1(1), 5 (2012). https://doi.org/10.1140/epjds5
Klimt, B., Yang, Y.: The enron corpus: a new dataset for email classification research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 217–226. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30115-8_22
Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Physica A: Stati. Mech. Appl. 390(6), 1150–1170 (2011)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arxiv:1301.3781 [cs.CL] (2013)
Mislove, A.E.: Online social networks: measurement, analysis, and applications to distributed information systems. Ph.D. thesis, Rice University (2009)
Pandhre, S., Mittal, H., Gupta, M., Balasubramanian, V.N.: STwalk: learning trajectory representations in temporal graphs. arXiv:1711.04150 [cs.SI] (2018)
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, New York (2014)
Starnini, M., Baronchelli, A., Barrat, A., Pastor-Satorras, R.: Random walks on temporal networks. Phys. Rev. E 85, 056115 (2012)
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. WWW 2015, pp. 1067–1077 (2015)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2016, pp. 1225–1234. ACM, New York (2016)
Zhiyuli, A., Liang, X., Xu, Z.: Learning distributed representations for large-scale dynamic social networks. In: IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, pp. 1–9, May 2017
Acknowledgments
The work presented in this paper was supported by the European Commission under contract H2020-700381 ASGARD.
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
Bastas, N., Semertzidis, T., Axenopoulos, A., Daras, P. (2019). evolve2vec: Learning Network Representations Using Temporal Unfolding. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-05710-7_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05709-1
Online ISBN: 978-3-030-05710-7
eBook Packages: Computer ScienceComputer Science (R0)