Advertisement

Which Topic Will You Follow?

  • Deqing Yang
  • Yanghua Xiao
  • Bo Xu
  • Hanghang Tong
  • Wei Wang
  • Sheng Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7524)

Abstract

Who are the most appropriate candidates to receive a call-for-paper or call-for-participation? What session topics should we propose for a conference of next year? To answer these questions, we need to precisely predict research topics of authors. In this paper, we build a MLR (Multiple Logistic Regression) model to predict the topic-following behavior of an author. By empirical studies, we find that social influence and homophily are two fundamental driving forces of topic diffusion in SCN (Scientific Collaboration Network). Hence, we build the model upon the explanatory variables representing above two driving forces. Extensive experimental results show that our model can consistently achieves good predicting performance. Such results are independent of the tested topics and significantly better than that of state-of-the-art competitor.

Keywords

topic-following social influence homophily SCN 

References

  1. 1.
    Provost, F.J., Dalessandro, B., Hook, R., Zhang, X., Murray, A.: Audience selection for on-line brand advertising: Privacy-friendly social network targeting. In: Proc. of SIGKDD (2009)Google Scholar
  2. 2.
    Roth, M., Ben-David, A., Deutscher, D., Flysher, G., Horn, I., Leichtberg, A., Leiser, N., Matias, Y., Merom, R.: Suggesting friends using the implicit social graph. In: Proc. of SIGKDD (2010)Google Scholar
  3. 3.
    Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: Proc. of SIGKDD (2008)Google Scholar
  4. 4.
    Crandall, D., Cosley, D., Kleinberg, J., Huttenlocher, D., Suri, S.: Feedback effects between similarity and social influence in online communities. In: Proc. of SIGKDD (2008)Google Scholar
  5. 5.
    McPherson, M., Smith-Lovin, L., Cook, J.: Birds of a feather: Homophily in social networks. Annual Review of Sociology 27, 415–445 (2001)CrossRefGoogle Scholar
  6. 6.
    Newman, M.E.J.: The structure of scientific collaboration networks. PNAS 98(2), 404–409 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Newman, M.E.J.: Scientific collaboration networks. ii. shortest paths, weighted networks, and centrality. Physical Review E 64(016132), 1–7 (2001)Google Scholar
  8. 8.
    Newman, M.E.J.: Coauthorship networks and patterns of scientific collaboration. PNAS 101, 5200–5205 (2004)CrossRefGoogle Scholar
  9. 9.
    Wu, B., Zhao, F., Yang, S., Suo, L., Tian, H.: Characterizing the evolution of collaboration network. In: Proc. of SWSM (2009)Google Scholar
  10. 10.
    Aral, S., Muchnika, L., Sundararajan, A.: Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. PNAS 106, 21544–21549 (2009)CrossRefGoogle Scholar
  11. 11.
    Rogers, E.: Diffusion of Innovations. Free Press (1995)Google Scholar
  12. 12.
    May, R.M., Lloyd, A.L.: Infection dynamics on scale-free networks. Physical Review E (2001)Google Scholar
  13. 13.
    Gomez-Rodriguez, M., Leskovec, J., Krause, A.: Inferring networks of diffusion and influence. In: Proc. of SIGKDD (2010)Google Scholar
  14. 14.
    Yang, J., Leskovec, J.: Modeling information diffusion in implicit networks. In: Proc. of ICDM (2010)Google Scholar
  15. 15.
    Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.: Information diffusion through blogspace. In: Proc. of SIGKDD (2004)Google Scholar
  16. 16.
    Lin, C.X., Zhao, B., Mei, Q., Han, J.: Pet: A statistical model for popular events tracking in social communities. In: Proc. of SIGKDD (2010)Google Scholar
  17. 17.
    Zhou, D., Ji, X., Zha, H., Giles, C.L.: Topic evolution and social interactions: How authors effect research. In: CIKM (2006)Google Scholar
  18. 18.
    He, Q., Chen, B., Pei, J., Qiu, B., Mitra, P., Giles, C.L.: Detecting topic evolution in scientific literature: How can citations help? In: Proc. of CIKM (2009)Google Scholar
  19. 19.
    Backstrom, L., Huttenlocher, D., Kleinberg, J.M., Lan, X.: Group formation in large social networks: Membership, growth, and evolution. In: Proc. of SIGKDD (2006)Google Scholar
  20. 20.
    Scholz, M.: Node similarity is the basic principle behind connectivity in complex networks. arXiv:1010.0803[physics.soc-ph] (2010)Google Scholar
  21. 21.
    Benjamin Golub, M.O.J.: How homophily affects diffusion and learning in networks. arXiv:0811.4013[physics.soc-ph] (2008)Google Scholar
  22. 22.
    Fond, T.L., Neville, J.: Randomization tests for distinguishing social influence and homophily effects. In: Proc. of WWW (2010)Google Scholar
  23. 23.
    Huang, J., Zhuang, Z., Li, J., Giles, C.L.: Collaboration over time: Characterizing and modeling network evolution. In: Proc. of WSDM (2008)Google Scholar
  24. 24.
    Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: Proc. of SIGKDD (2009)Google Scholar
  25. 25.
    Peng, H.-K., Zhu, J., Piao, D., Yan, R., Zhang, J.Y.: Retweet modeling using conditional random fields. In: Proc. of ICDM Workshop (2011)Google Scholar
  26. 26.
    Macskassy, S.A., Michelson, M.: Why do people retweet? anti-homophily wins the day! In: Proc. of ICWSM (2011)Google Scholar
  27. 27.
    Choudhury, M.D., Sundaram, H., John, A., Seligmann, D.D.: Contextual prediction of communication flow in social networks. In: Proc. of WIC (2007)Google Scholar
  28. 28.
    Harris, D., Christopher, J.C., Linda, K., Smola Alexander, J., Vapnik, V.: Support vector regression machines. In: NIPS, pp. 155–161 (1996)Google Scholar
  29. 29.
    Agresti, A.: Categorical data analysis. Wiley, Berlin (2002)zbMATHCrossRefGoogle Scholar
  30. 30.
    Kschischang, F.R., Member, S., Frey, B.J., Andrea Loeliger, H.: Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory 47, 498–519 (2001)zbMATHCrossRefGoogle Scholar
  31. 31.
    Goetz, M., Leskovec, J., McGlohon, M., Faloutsos, C.: Information propagation and network evolution on the web. In: Proc. of CWSM (2009)Google Scholar
  32. 32.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann (2006)Google Scholar
  33. 33.
    Mark, G.: Receiver operating characteristic (roc) plots: Fundamental evaluation tool in clinical medicine. Clin. Chem. 30(4), 561–567 (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Deqing Yang
    • 1
  • Yanghua Xiao
    • 1
  • Bo Xu
    • 1
  • Hanghang Tong
    • 2
  • Wei Wang
    • 1
  • Sheng Huang
    • 3
  1. 1.School of Computer ScienceFudan UniversityShanghaiP.R. China
  2. 2.IBM T.J. Watson Research CenterUSA
  3. 3.IBM China Research LabP.R.China

Personalised recommendations