Skip to main content

Fast Sequence-Based Embedding with Diffusion Graphs

  • Conference paper
  • First Online:
Complex Networks IX (CompleNet 2018)

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

Included in the following conference series:

Abstract

A graph embedding is a representation of graph vertices in a low- dimensional space, which approximately preserves properties such as distances between nodes. Vertex sequence-based embedding procedures use features extracted from linear sequences of nodes to create embeddings using a neural network. In this paper, we propose diffusion graphs as a method to rapidly generate vertex sequences for network embedding. Its computational efficiency is superior to previous methods due to simpler sequence generation, and it produces more accurate results. In experiments, we found that the performance relative to other methods improves with increasing edge density in the graph. In a community detection task, clustering nodes in the embedding space produces better results compared to other sequence-based embedding methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Agarwal, N., Liu, H., Murthy, S., Sen, A., Wang, X.: A social identity approach to identify familiar strangers in a social network. In: ICWSM (2009)

    Google Scholar 

  2. Alon, N., Avin, C., Kouckỳ, M., Kozma, G., Lotker, Z., Tuttle, M.R.: Many random walks are faster than one. Comb. Probab. Comput. 20(4), 481–502 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  3. Chatr-Aryamontri, A., Breitkreutz, B.J., Oughtred, R., Boucher, L., et al.: The biogrid interaction database: 2015 update. Nucleic Acids Res. 43(D1), D470–D478 (2014)

    Article  Google Scholar 

  4. Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)

    Article  ADS  Google Scholar 

  5. Fire, M., Tenenboim, L., Lesser, O., Puzis, R., Rokach, L., Elovici, Y.: Link prediction in social networks using computationally efficient topological features. In: IEEE Third Inernational Conference on Social Computing (SocialCom), pp. 73–80. IEEE (2011)

    Google Scholar 

  6. Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: A survey. arXiv:1705.02801 (2017)

  7. 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 (2016)

    Google Scholar 

  8. Herman, I., Melançon, G., Marshall, M.S.: Graph visualization and navigation in information visualization: a survey. IEEE Trans. Vis. Comput. Graph. 6(1), 24–43 (2000)

    Article  Google Scholar 

  9. Mahoney, M.: Large text compression benchmark (2011)

    Google Scholar 

  10. McAuley, J., Leskovec, J.: Image labeling on a network: using social-network metadata for image classification. In: Computer Vision-ECCV, pp. 828–841 (2012)

    Google Scholar 

  11. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301.3781 (2013)

  12. Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)

    Article  ADS  Google Scholar 

  13. 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 (2014)

    Google Scholar 

  14. Pons, P., Latapy, M.: Computing communities in large networks using random walks. J. Graph Algorithms Appl. 10(2), 191–218 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  15. R Sarkar, Yin, X., Gao, J., Luo, F., Gu, X.D.: Greedy routing with guaranteed delivery using ricci flows. In: International Conference on Information Processing in Sensor Networks (IPSN), pp. 121–132. ACM (2009)

    Google Scholar 

  16. Shang, Y., Ruml, W., Zhang, Y., Fromherz, M.P.J.: Localization from mere connectivity. In: Proceedings of the 4th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 201–212. ACM (2003)

    Google Scholar 

  17. West, D.B., et al.: Introduction to Graph Theory. Prentice hall, Upper Saddle River (2001)

    Google Scholar 

  18. White, S., Smyth, P.: A spectral clustering approach to finding communities in graphs. In: Proceedings of the 2005 SIAM International Conference on Data Mining, pp. 274–285. SIAM (2005)

    Google Scholar 

  19. Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42(1), 181–213 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

Benedek Rozemberczki was supported by the Centre for Doctoral Training in Data Science, funded by EPSRC (grant EP/L016427/1).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Benedek Rozemberczki .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rozemberczki, B., Sarkar, R. (2018). Fast Sequence-Based Embedding with Diffusion Graphs. In: Cornelius, S., Coronges, K., Gonçalves, B., Sinatra, R., Vespignani, A. (eds) Complex Networks IX. CompleNet 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-73198-8_9

Download citation

Publish with us

Policies and ethics