Abstract
Large-scale network mining and analysis is key to revealing the underlying dynamics of networks. Lately, there has been a fast-growing interest in learning random walk-based low-dimensional continuous representations of networks. While these methods perform well, they can only operate on static networks. In this paper, we propose a random-walk based method for learning representations of evolving networks. The key idea of our approach is to maintain a set of random walks that are consistently valid with respect to the updated network topology. This way we are able to continuously learn a new mapping function from the new network to the existing low-dimension network representation. A thorough experimental evaluation is performed that demonstrates that our method is both accurate and fast, for a varying range of conditions.
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
Notes
- 1.
node2vec; code is available at https://github.com/aditya-grover/node2vec
- 2.
References
Ahmed, A., Shervashidze, N., Narayanamurthy, S., Josifovski, V., Smola, A.J.: Distributed large-scale natural graph factorization. ACM (2013)
Antoniak, M., Mimno, D.: Evaluating the stability of embedding-based word similarities. TACL 6, 107–119 (2018)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE TPAMI 35(8), 1798–1828 (2013)
Breitkreutz, B.J., et al.: The biogrid interaction database: 2008 update. Nucleic Acids Res. 36(suppl 1), D637–D640 (2007)
Cai, H., Zheng, V.W., Chang, K.: A comprehensive survey of graph embedding: problems, techniques and applications. IEEE TKDE (2018)
Goyal, P., Kamra, N., He, X., Liu, Y.: Dyngem: deep embedding method for dynamic graphs. In: IJCAI Workshop on Representation Learning for Graphs (2018)
Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: KDD, pp. 855–864 (2016)
Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. CoRR abs/1605.09096 (2016)
Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: methods and applications. IEEE Data Eng. Bull. 40(3), 52–74 (2017)
Jiang, M., Fu, A.W.C., Wong, R.C.W.: Reads: a random walk approach for efficient and accurate dynamic simrank. Proc. VLDB Endow. 10(9), 937–948 (2017)
Kim, Y., Chiu, Y., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. CoRR abs/1405.3515 (2014)
Li, J., Dani, H., Hu, X., Tang, J., Chang, Y., Liu, H.: Attributed network embedding for learning in a dynamic environment. In: CIKM, pp. 387–396 (2017)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 3111–3119(2013)
Newman, M.E.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. CoRR abs/1403.6652 (2014)
Reza, Z., Huan, L.: Social computing data repository. http://socialcomputing.asu.edu/
Trivedi, R., Dai, H., Wang, Y., Song, L.: Know-evolve: deep temporal reasoning for dynamic knowledge graphs. ICML 70, 3462–3471 (2017)
Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. KAIS 42(1), 181–213 (2015)
Zhang, D., Yin, J., Zhu, X., Zhang, C.: Network representation learning: a survey. IEEE Trans. Big Data (2018)
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
Heidari, F., Papagelis, M. (2019). EvoNRL: Evolving Network Representation Learning Based on Random Walks. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham. https://doi.org/10.1007/978-3-030-05411-3_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-05411-3_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05410-6
Online ISBN: 978-3-030-05411-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)