Skip to main content
Log in

Community detection and co-author recommendation in co-author networks

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

With the increasing complexity of scientific research and the expanding scale of projects, scientific research cooperation is an important trend in large-scale research. The analysis of co-authorship networks is a big data problem due to the expanding scale of the literature. Without sufficient data mining, research cooperation will be limited to a similar group, namely, a “small group”, in the co-author networks. This “small group” limits the research results and openness. However, the researchers are not aware of the existence of other researchers due to insufficient big data support. Considering the importance of discovering communities and recommending potential collaborations from a large body of literature, we propose an enhanced clustering algorithm for detecting communities. It includes the selection of an initial central node and the redefinition of the distance and iteration of the central node. We also propose a method that is based on the hilltop algorithm, which is an algorithm that is used in search engines, for recommending co-authors via link analysis. The co-author candidate set is improved by screening and scoring. In screening, the expert set formation of the hilltop algorithm is added. The score is calculated from the durations and quantity of the collaborations. Via experiments, communities can be extracted, and co-authors can be recommended from the big data of the scientific research literature.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Barber MJ, Clark JW (2009) Detecting network communities by propagating labels under constraints. Phys Rev E 80(2):026129

    Article  Google Scholar 

  2. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech 2008(10):155–168

    Article  Google Scholar 

  3. Cardoso B, Sedrakyan G, Gutiérrez F, Parra D, Brusilovsky P, Verbert K (2019) Intersectionexplorer, a multi-perspective approach for exploring recommendations. Int J Hum-Comput Stud 121:73–92

    Article  Google Scholar 

  4. Celebi ME, Kingravi HA, Vela PA (2013) A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst Appl 40(1):200–210

    Article  Google Scholar 

  5. Chauhan S, Girvan M, Ott E (2009) Spectral properties of networks with community structure. Phys Rev E 80(5):056114

    Article  Google Scholar 

  6. Chen S, Wang Z-Z, Tang L, Tang Y-N, Gao Y-Y, Li H-J, Xiang J, Zhang Y (2018) Global vs local modularity for network community detection. PloS One 13(10):e0205284

    Article  Google Scholar 

  7. Donetti L, Munoz MA (2004) Detecting network communities: a new systematic and efficient algorithm. J Stat Mech Theory Exp 2004(10):P10012

    Article  Google Scholar 

  8. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3–5):75–174

    Article  MathSciNet  Google Scholar 

  9. Kim J, Hastak M (2018) Social network analysis: characteristics of online social networks after a disaster. Int J Inf Manag 38(1):86–96

    Article  Google Scholar 

  10. Lancichinetti A, Fortunato S, Kertész J (2009) Detecting the overlapping and hierarchical community structure in complex networks. New J Phys 11(3):033015

    Article  Google Scholar 

  11. Lancichinetti A, Radicchi F, Ramasco JJ, Fortunato S (2011) Finding statistically significant communities in networks. PLoS One 6(4):e18961

    Article  Google Scholar 

  12. Li Y, Jia C, Jian Y (2015) A parameter-free community detection method based on centrality and dispersion of nodes in complex networks. Phys A Stat Mech Appl 438:321–334

    Article  Google Scholar 

  13. Lopes, G. R., Moro, M. M., Wives, L. K., De Oliveira, J. P. M. Collaboration recommendation on academic social networks. In: International Conference on Conceptual Modeling (2010), Springer, pp. 190–199

  14. Martin R, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci USA 105(4):1118–1123

    Article  Google Scholar 

  15. Newman ME (2004) Coauthorship networks and patterns of scientific collaboration. Proc Natl Acad Sci 101(suppl 1):5200–5205

    Article  Google Scholar 

  16. Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113

    Article  Google Scholar 

  17. Parthasarathy S, Ruan Y, Satuluri V (2011) Community discovery in social networks: Applications, methods and emerging trends. Social Network Data Analytics 79–113

  18. Pecli A, Cavalcanti MC, Goldschmidt R (2018) Automatic feature selection for supervised learning in link prediction applications: a comparative study. Knowl Inform Syst 56(1):85–121

    Article  Google Scholar 

  19. Ren Z-M, Zeng A, Zhang Y-C (2018) Structure-oriented prediction in complex networks. Phys Rep

  20. Tian B, Li W (2018) Community detection method based on mixed-norm sparse subspace clustering. Neurocomputing 275:2150–2161

    Article  Google Scholar 

  21. Tibély G, Kertész J (2008) On the equivalence of the label propagation method of community detection and a potts model approach. Phys A Stat Mech Appl 387(19–20):4982–4984

    Article  Google Scholar 

  22. Wang J, Yue F, Wang G, Xu Y, Yang C (2015) Expert recommendation in scientific social network based on link prediction. J Intell 34(6):151–156

    Google Scholar 

  23. Wang Q, Li W, Zhang X, Lu S (2016) Academic paper recommendation based on community detection in citation-collaboration networks. In: Web Technologies and Applications (Cham), F. Li, K. Shim, K. Zheng, and G. Liu, Eds., Springer International Publishing, pp. 124–136

  24. Welch E, Melkers J (2006) Effects of network size and gender on pi grant awards to scientists and engineers: an analysis from a national survey of five fields. In: Annual Meeting of the Association for Public Policy and Management (APPAM)

  25. Yang B, Li X, Liu X, He H, Chen W (2019) Alternating between consensus and leader selection reveals community structure in networks. Phys A Stat Mech Appl 515:693–706

    Article  Google Scholar 

  26. Zhao J, Dong K, Yu J, Kai N (2013) Social network analysis technologies in e-science. E-Sci Technol Appl

  27. Zhao Y-D, Zhou C (2011) The cooperation network of chinese researchers: a perspective of ego-centered social network analysis. Stud Sci Sci 7:999–1006

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jianjun Yu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This research is supported by NSFC Grant No.61836013 and CAS 135 Informatization Project XXH13504.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jin, T., Wu, Q., Ou, X. et al. Community detection and co-author recommendation in co-author networks. Int. J. Mach. Learn. & Cyber. 12, 597–609 (2021). https://doi.org/10.1007/s13042-020-01190-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-020-01190-8

Keywords

Navigation