Improved Community Interaction Through Context Based Citation Analysis

  • Baishali Saha
  • Tanushree Anand
  • Anurag Sharma
  • Bibhas Ghoshal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10682)

Abstract

Traditional citation networks which form the basis of study of community interaction tend to leave out a lot of articles which are related to a community but have not been directly cited by the members of it. As a result, the parameters estimated during the study of community interaction remain fairly inaccurate. In this work, we tend to perform a more accurate community interaction study by proposing a context-aware citation network which allows inclusion of papers to a community which have both direct as well as indirect relevance to the existing members of the community. A comparative analysis of computer science community networks built upon the proposed citation network and traditional citation network using the CiteSeer dataset show about 20–30% better results in favour of the former.

References

  1. 1.
    Chen, P., Redner, S.: Community structure of the physical review citation network. J. Inf. 4(3), 278–290 (2010). http://www.sciencedirect.com/science/article/pii/S1751157710000027 CrossRefGoogle Scholar
  2. 2.
    Chakrabort, T., Sikdar, S., Tammana, V., Ganguly, N., Mukherjee, A.: Computer science fields as ground-truth communities: their impact, rise and fall. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, New York, NY, USA, pp. 426–433. ACM (2013). http://doi.acm.org/10.1145/2492517.2492536
  3. 3.
    He, Q., Pei, J., Kifer, D., Mitra, P., Giles, L.: Context-aware citation recommendation. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, New York, NY, USA, pp. 421–430. ACM, (2010). http://doi.acm.org/10.1145/1772690.1772734
  4. 4.
    Huang, W., Wu, Z., Chen, L., Mitra, P., Giles, C.L.: A neural probabilistic model for context based citation recommendation. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 25–30 January 2015, Austin, Texas, USA, pp. 2404–2410 (2015). http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9737
  5. 5.
    Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42(1), 181–213 (2015). New York, NY, USA: Springer-Verlag. https://doi.org/10.1007/s10115-013-0693-z CrossRefGoogle Scholar
  6. 6.
    Caragea, C., Bulgarov, F.A., Godea, A., Gollapalli, S.D.: Citation-enhanced keyphrase extraction from research papers: a supervised approach. In: EMNLP, vol. 14, pp. 1435–1446 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Baishali Saha
    • 1
  • Tanushree Anand
    • 1
  • Anurag Sharma
    • 1
  • Bibhas Ghoshal
    • 1
  1. 1.Department of Information TechnologyIndian Institute of Information Technology, AllahabadAllahabadIndia

Personalised recommendations