Advertisement

Frontiers of Computer Science

, Volume 13, Issue 1, pp 212–214 | Cite as

Community detection in scientific collaborative network with bayesian matrix learning

  • Xiaohua Shi
  • Hongtao LuEmail author
Letter
  • 10 Downloads

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This work was supported by NSFC (61772330), the Science and Technology Commission of Shanghai Municipality (16Z111040011), China Next Generation Internet IPv6 project (NGII20170609), and Arts and Science Cross Special Fund of Shanghai Jiao Tong University (15JCMY08).

Supplementary material

11704_2018_8124_MOESM1_ESM.ppt (252 kb)
Supplementary material, approximately 250 KB.

References

  1. 1.
    Newman M E J. Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 2004, 101(suppl 1): 5200–5205Google Scholar
  2. 2.
    Danon L, Díaz-Guilera A, Arenas A. The effect of size heterogeneity on community identification in complex networks. Journal of Statistical Mechanics: Theory and Experiment, 2006, 2006(11): P11010CrossRefGoogle Scholar
  3. 3.
    Lu H T, Fu Z Y, Shu X. Non-negative and sparse spectral clustering. Pattern Recognition, 2014, 47(1): 418–426CrossRefzbMATHGoogle Scholar
  4. 4.
    Blondel V D, Guillaume J L, Lambiotte R, Lefebvre, E. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008, 2008(10): P10008CrossRefGoogle Scholar
  5. 5.
    Le Martelot E, Hankin C. Fast multi-scale detection of relevant communities in large-scale networks. The Computer Journal, 2013, 56(9): 1136–1150CrossRefGoogle Scholar
  6. 6.
    Lee D D, Seung H S. Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 2001, 13: 556–562Google Scholar
  7. 7.
    Wang F, Li T, Wang X, Chris D. Community discovery using nonnegative matrix factorization. Data Mining and Knowledge Discovery, 2011, 22(3): 493–521MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Cemgil A T. Bayesian inference for nonnegative matrix factorisation models. Computational Intelligence and Neuroscience, 2009, 2009: 1–17CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer ScienceShanghai Jiaotong UniversityShanghaiChina
  2. 2.LibraryShanghai Jiaotong UniversityShanghaiChina

Personalised recommendations