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Learning Structural Node Representations on Directed Graphs

  • Niklas Steenfatt
  • Giannis Nikolentzos
  • Michalis Vazirgiannis
  • Qiang Zhao
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)

Abstract

Many applications require identifying nodes that perform similar functions in a graph. Learning latent representations that capture such structural role information about nodes has recently gained a lot of attention. A state-of-the-art algorithm, struc2vec, generates such representations for the nodes of undirected networks. However, the algorithm is unable to handle directed, weighted networks. In this paper, we present struc2vec++, a generalization of the above algorithm to such types of networks. We evaluate struc2vec++ on real and synthetic networks. We show that taking into account edge directions greatly improves performance. We compare struc2vec++ against a recently proposed algorithm. Although struc2vec++ is in most cases outperformed by the competing algorithm, experiments in a variety of different scenarios demonstrate that it is much more memory efficient and it can better capture structural roles in the presence of noise.

Keywords

Role discovery Node embeddings Structural identity 

References

  1. 1.
    Aliabadi, A.Z., Razzaghi, F., Kochak, S.P.M., Ghorbani, A.A.: Classifying organizational roles using email social networks. In: Canadian Conference on Artificial Intelligence, pp. 301–307 (2013)Google Scholar
  2. 2.
    Bartoletti, M., Pes, B., Serusi, S.: Data mining for detecting Bitcoin Ponzi schemes. arXiv:1803.00646 (2018)
  3. 3.
    Buntain, C., Golbeck, J.: Identifying social roles in reddit using network structure. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 615–620 (2014)Google Scholar
  4. 4.
    Cai, H., Zheng, V.W., Chang, K.: A comprehensive survey of graph embedding: problems, techniques and applications. IEEE Trans. Knowl. Data Eng. (2018)Google Scholar
  5. 5.
    Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings of the 24th International Conference on Information and Knowledge Management, pp. 891–900 (2015)Google Scholar
  6. 6.
    Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. Int. J. Pattern Recognit. Artif. Intell. 18(03), 265–298 (2004)CrossRefGoogle Scholar
  7. 7.
    Donnat, C., Zitnik, M., Hallac, D., Leskovec, J.: Learning structural node embeddings via diffusion wavelets. In: Proceedings of the 24rd International Conference on Knowledge Discovery and Data Mining, pp. 1320–1329 (2018)Google Scholar
  8. 8.
    Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)Google Scholar
  9. 9.
    Henderson, K., et al.: RolX: structural role extraction & mining in large graphs. In: Proceedings of the 18th International Conference on Knowledge Discovery and Data Mining, pp. 1231–1239 (2012)Google Scholar
  10. 10.
    McCallum, A., Wang, X., Corrada-Emmanuel, A.: Topic and role discovery in social networks with experiments on enron and academic email. J. Artif. Intell. Res. 30, 249–272 (2007)CrossRefGoogle Scholar
  11. 11.
    Narayanan, A., Chandramohan, M., Chen, L., Liu, Y., Saminathan, S.: subgraph2vec: learning distributed representations of rooted sub-graphs from large graphs. In: Proceedings of the 12th International Workshop on Mining and Learning with Graphs (2016)Google Scholar
  12. 12.
    Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)Google Scholar
  13. 13.
    Ribeiro, L.F., Saverese, P.H., Figueiredo, D.R.: struc2vec: learning node representations from structural identity. In: Proceedings of the 23rd International Conference on Knowledge Discovery and Data Mining, pp. 385–394 (2017)Google Scholar
  14. 14.
    Rossi, R.A., Ahmed, N.K.: Role discovery in networks. IEEE Trans. Knowl. Data Eng. 27(4), 1112–1131 (2015)Google Scholar
  15. 15.
    Salvador, S., Chan, P.: FastDTW: toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)CrossRefGoogle Scholar
  16. 16.
    Shokoohi-Yekta, M., Hu, B., Jin, H., Wang, J., Keogh, E.: Generalizing DTW to the multi-dimensional case requires an adaptive approach. Data Min. Knowl. Discov. 31(1), 1–31 (2017)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Tsitsulin, A., Mottin, D., Karras, P., Müller, E.: VERSE: versatile graph embeddings from similarity measures. In: Proceedings of the 2018 World Wide Web Conference, pp. 539–548 (2018)Google Scholar
  18. 18.
    Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 203–209 (2017)Google Scholar
  19. 19.
    Watts, D.J.: Small Worlds: the Dynamics of Networks Between Order and Randomness. Princeton University Press, Princeton (2000)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Niklas Steenfatt
    • 1
  • Giannis Nikolentzos
    • 1
  • Michalis Vazirgiannis
    • 1
    • 2
  • Qiang Zhao
    • 3
  1. 1.École PolytechniquePalaiseauFrance
  2. 2.Athens University of Economics and BusinessAthensGreece
  3. 3.TencentShenzhenChina

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