Advertisement

A Method of Intention Discovery Based on Scientific Collaboration Information

  • Ning ZhangEmail author
  • Chenli Zhao
  • Xue Zhang
  • Dongyun Yi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

Intention usually refers to the intention to achieve a certain purpose, which is the realistic power to motivate people to act. At present, with the development of the Internet and social media, the scope of human activities is becoming larger and deeper. In the academic circle, the cooperation between scientific research workers is becoming closer and closer, and the activities of human beings and even the cooperative behaviors of scientific research workers contain the intentions of individuals or organizations. By extracting scientific citation data on the network, this paper constructs a spatial-temporal related scientific collaboration network, proposes a spatial-temporal distribution method of resources, and analyses and evaluates the behavioral intention of collaboration network. It mainly includes the visualization method of spatial-temporal distribution of resources of paper information, the spatial-temporal network modeling of paper cooperative intention, the analysis of intention evaluation model and so on. Finally, the problems and possible challenges in the future research are prospected.

Keywords

Spatial and Temporal distribution Collaboration network Intention analysis 

Notes

Acknowledgments

This work is partially supported by the National Key R&D Program of China (Grant No. 2017YCF1200301).

References

  1. 1.
    Barabási, A.-L.: The new science of networks. Phys. Today 6(5), 243–270 (2003).  https://doi.org/10.2307/20033300CrossRefGoogle Scholar
  2. 2.
    Watts, D.J.: The “new” science of networks. Ann. Rev. Sociol. 30(1), 243–270 (2004).  https://doi.org/10.1146/annurev.soc.30.020404.104342CrossRefGoogle Scholar
  3. 3.
    Dereian, P., Hummon, P.: North-Holland connectivity in a citation network: the development of dna theory*. Soc. Netw. 11(1), 39–63 (1989).  https://doi.org/10.1016/0378-8733(89)90017-8CrossRefGoogle Scholar
  4. 4.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003).  https://doi.org/10.1137/S003614450342480MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Kretschmer, H., Kretschmer, T.: A new centrality measure for social network analysis applicable to bibliometric and webometric data. In: International Conference on Digital Information Management IEEE (2006).  https://doi.org/10.1109/ICDIM.2007.369353
  6. 6.
    Xiaoming, L., et al.: Co-authorship networks in the digital library research community. Inf. Process. Manag. 41(6), 1462–1480 (2005).  https://doi.org/10.1016/j.ipm.2005.03.012CrossRefGoogle Scholar
  7. 7.
    Börner, K., et al.: Studying the emerging global brain: analyzing and visualizing the impact of co-authorship teams. Complexity 10(4), 57–67 (2005).  https://doi.org/10.1002/cplx.20078CrossRefGoogle Scholar
  8. 8.
    Ichise, R., Takeda, H., Muraki, T.: A discovery method of research communities. In: Proceedings of Adaptation in Artificial Biological Systems, no. 3, pp. 1 28–1 31 (2006)Google Scholar
  9. 9.
    Ichise, R., Takeda, H., Muraki, T.: Research community mining with topic identification. In: International Conference on Information Visualization (2006)Google Scholar
  10. 10.
    Basu, P., et al.: Modeling and analysis of time-varying graphs. arXiv preprint arXiv:1012.0260 (2010)
  11. 11.
    Kolar, M., et al.: Estimating time-varying networks. Ann. Appl. Stat. 4(1), 94–123 (2010).  https://doi.org/10.1214/09-AOAS308MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Martin, R., Bergstrom, C.T., Fabio, R.: Mapping change in large networks. PLoS One 5(1), e8694 (2010).  https://doi.org/10.1371/journal.pone.0008694CrossRefGoogle Scholar
  13. 13.
    Casteigts, A., et al.: Time-varying graphs and dynamic networks. Int. J. Parallel Emergent Distrib. Syst. 27(5), 387–408 (2010)CrossRefGoogle Scholar
  14. 14.
    Holme, P., Saramäki, J.: Temporal networks as a modeling framework. Understanding Complex Systems, pp. 1–14 (2013)Google Scholar
  15. 15.
    Bassett, D.S., et al.: Cross-linked structure of network evolution. Chaos 24(1), 97–386 (2014).  https://doi.org/10.1063/1.4858457MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ning Zhang
    • 1
    Email author
  • Chenli Zhao
    • 1
  • Xue Zhang
    • 1
  • Dongyun Yi
    • 1
    • 2
  1. 1.College of Liberal Arts and SciencesNational University of Defense TechnologyChangshaChina
  2. 2.State Key Laboratory of High Performance ComputingNational University of Defense TechnologyChangshaChina

Personalised recommendations