Abstract
Graph embedding is an important method for learning low-dimensional representations of vertices in graph data. The problem of graph embedding requires that a better embedding method be used to optimize the corresponding objective function. There are two challenges associated with graph embedding. First, the optimization algorithm is based on gradient descent and falls easily into the local optimum. Second, whether the objective function design is reasonable has a huge impact on the embedding results. To tackle this two challenges, evolutionary strategies are used as the optimization algorithm for graph embedding. Evolutionary strategies do not need to know the specific analytical form of the objective function, and can effectively overcome the challenge of the problem of optimum. In addition, to tackle the challenge of the objective function, this paper improves on the design of the objective function based on the previous research. To verify the effectiveness of the algorithm, experiments on multi-label classification tasks were carried out on four real network data sets. Experiments show the effectiveness and potential of evolutionary strategy for graph embedding.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
The data can be found from CogDL, which an Extensive Research Toolkit for deep Learning on Graphs.
References
Cao, S., Lu, W., Xu, Q.: Deep neural networks for learning graph representations. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
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 (2016)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995-International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)
Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD 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: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Price, K.V.: Differential evolution. In: Zelinka, I., Snasel, V., Abraham, A. (eds) Handbook of Optimization, vol. 38, pp. 187–214. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-30504-7_8
Sun, L., Ji, S., Ye, J.: Hypergraph spectral learning for multi-label classification. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 668–676 (2008)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE. In: Proceedings of the 24th International Conference on World Wide Web - WWW 2015 (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, pp. 1225–1234 (2016)
Zhang, Z., Shao, L., Xu, Y., Liu, L., Yang, J.: Marginal representation learning with graph structure self-adaptation. IEEE Trans. Neural Netw. Learn. Syst. 29(10), 4645–4659 (2018)
Zhou, J., et al.: Graph neural networks: a review of methods and applications. arXiv preprint arXiv:1812.08434 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Jin, J., Yu, D. (2020). Evolutionary Strategy for Graph Embedding. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-65390-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65389-7
Online ISBN: 978-3-030-65390-3
eBook Packages: Computer ScienceComputer Science (R0)