Skip to main content
Log in

Learning distributed representations for community search using node embedding

  • Letter
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

  1. Sozio M, Gionis A. The community-search problem and how to plan a successful cocktail party. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2010, 939–948

    Chapter  Google Scholar 

  2. 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. 2014, 701–710

    Google Scholar 

  3. 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. 2016, 855–864

    Google Scholar 

  4. Ma L, Huang H, He Q, Chiew K, Wu J, Che Y. GMAC: a seedinsensitive approach to local community detection. In: Proceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery. 2013, 297–308

    Chapter  Google Scholar 

  5. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. In: Proceedings of the 1st International Conference on Learing Representations. 2013

    Google Scholar 

  6. Clauset A. Finding local community structure in networks. Physical Review E, 2005, 72(2): 026132

    Article  Google Scholar 

  7. Luo F, Wang J Z, Promislow E. Exploring local community structures in large networks. Web Intelligence and Agent Systems, 2008, 6(4): 387–400

    Google Scholar 

  8. Tang L, Liu H. Leveraging social media networks for classification. Data Mining and Knowledge Discovery, 2011, 23(3): 447–478

    Article  MathSciNet  MATH  Google Scholar 

  9. Lancichinetti A, Fortunato S, Radicchi F. Benchmark graphs for testing community detection algorithms. Physical Review E, 2008, 78(4): 046110

    Article  Google Scholar 

Download references

Acknowledgements

The project was supported by the National Key R&D Program of China (2018YFB1004700), and the National Natural Science Foundation of China (Grant Nos. 61772122, 61872074).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daling Wang.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, J., Wang, D., Feng, S. et al. Learning distributed representations for community search using node embedding. Front. Comput. Sci. 13, 437–439 (2019). https://doi.org/10.1007/s11704-018-7389-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-018-7389-1

Navigation