Abstract
In the real world, various information can be represented by graph structure data. For example, interpersonal relationships and protein structure. In recent years, with the development of artificial intelligence, graph embedding has become a popular method of network analysis. It can reduce the dimension of network structure data, so that network structure data can be applied to various machine learning and deep learning tasks. At the same time, many studies of network geometry show that the hidden metric of many complex networks is hyperbolic. After hyperbolic space mapping, nodes in the original network data structure can be represented by hyperbolic coordinates. Hyperbolic coordinates contain information about the popularity and similarity of nodes which is very important for unsupervised clustering tasks. However, the random walk strategy in the native DeepWalk algorithm cannot effectively extract this information. So we propose an improvement of the DeepWalk algorithm based on hyperbolic coordinates and achieved good results on many datasets.
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
Keogh, E., Mueen, A.: Curse of dimensionality. In: Gass, S.I., Fu, M.C. (eds.) Encyclopedia of Machine Learning, pp. 257–258. Springer, Boston (2011). https://doi.org/10.1007/978-1-4419-1153-7_200110
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Cambria, E., White, B.: Jumping NLP curves: a review of natural language processing research. IEEE Comput. Intell. Mag. 9(2), 48–57 (2014)
Cai, H., Zheng, V.W., Chang, K.: A comprehensive survey of graph embedding: problems, techniques and applications. IEEE Trans. Knowl. Data Eng. 30, 1616–1637 (2018)
Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: a survey. Knowl. Based Syst. 151, 78–94 (2018)
Grbovic, M., et al.: E-commerce in your inbox: product recommendations at scale. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1809–1818. ACM, August 2015
Barkan, O., Koenigstein, N.: Item2Vec: neural item embedding for collaborative filtering. In: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6. IEEE, September 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. ACM, August 2016
Ding, W., Lin, C., Ishwar, P.: Node embedding via word embedding for network community discovery. IEEE Trans. Signal Inf. Process. Netw. 3(3), 539–552 (2017)
Kooti, F., Grbovic, M., Aiello, L.M., Bax, E., Lerman, K.: iPhone’s digital marketplace: characterizing the big spenders. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 13–21. ACM, February 2017
Chamberlain, B.P., Cardoso, A., Liu, C.H., Pagliari, R., Deisenroth, M.P.: Customer lifetime value prediction using embeddings. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1753–1762. ACM, August 2017
Perozzi, B., Kulkarni, V., Skiena, S.: Walklets: multiscale graph embeddings for interpretable network classification. arXiv preprint arXiv:1605.02115 (2016)
Li, J., Zhu, J., Zhang, B.: Discriminative deep random walk for network classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1004–1013 (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. ACM, August 2014
Cheng, W., Greaves, C., Warren, M.: From n-gram to SkipGram to concgram. Int. J. Corpus Linguist. 11(4), 411–433 (2006)
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, May 2015
Chamberlain, B.P., Clough, J., Deisenroth, M.P.: Neural embeddings of graphs in hyperbolic space. arXiv preprint arXiv:1705.10359 (2017)
Krioukov, D., Papadopoulos, F., Kitsak, M., Vahdat, A., Boguná, M.: Hyperbolic geometry of complex networks. Phys. Rev. E 82(3), 036106 (2010)
Papadopoulos, F., Kitsak, M., Serrano, M.Á., Boguná, M., Krioukov, D.: Popularity versus similarity in growing networks. Nature 489(7417), 537 (2012)
Papadopoulos, F., Psomas, C., Krioukov, D.: Network mapping by replaying hyperbolic growth. IEEE/ACM Trans. Netw. (TON) 23(1), 198–211 (2015)
Lovász, L.: Random walks on graphs: a survey. In: Combinatorics, Paul Erdos Is Eighty, vol. 2(1), pp. 1–46 (1993)
Duc, D.M., van Hieu, N.: Graphs with prescribed mean curvature on Poincaré disk. Bull. Lond. Math. Soc. 27(4), 353–358 (1995)
Newman, M.E.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74(3), 036104 (2006)
Scikit-learn developers. Custering. https://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation. Accessed 24 Feb 2019
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
Yu, S., Wu, Y., Song, Y., Jiang, G., Su, X. (2019). Application of DeepWalk Based on Hyperbolic Coordinates on Unsupervised Clustering. In: Liu, F., Xu, J., Xu, S., Yung, M. (eds) Science of Cyber Security. SciSec 2019. Lecture Notes in Computer Science(), vol 11933. Springer, Cham. https://doi.org/10.1007/978-3-030-34637-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-34637-9_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34636-2
Online ISBN: 978-3-030-34637-9
eBook Packages: Computer ScienceComputer Science (R0)