SocNL: Bayesian Label Propagation with Confidence

  • Yuto Yamaguchi
  • Christos Faloutsos
  • Hiroyuki Kitagawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9077)


How can we predict Smith’s main hobby if we know the main hobby of Smith’s friends? Can we measure the confidence in our prediction if we are given the main hobby of only a few of Smith’s friends? In this paper, we focus on how to estimate the confidence on the node classification problem. Providing a confidence level for the classification problem is important because most nodes in real world networks tend to have few neighbors, and thus, a small amount of evidence. Our contributions are three-fold: (a) novel algorithm; we propose a semi-supervised learning algorithm that converges fast, and provides the confidence estimate (b) theoretical analysis; we show the solid theoretical foundation of our algorithm and the connections to label propagation and Bayesian inference (c) empirical analysis; we perform extensive experiments on three different real networks. Specifically, the experimental results demonstrate that our algorithm outperforms other algorithms on graphs with less smoothness and low label density.


Bayesian Inference Target Node Dirichlet Distribution Label Propagation Label Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Adamic, L.A., Glance, N.: The political blogosphere and the 2004 us election: divided they blog. In: Proceedings of the 3rd International Workshop on Link Discovery, pp. 36–43. ACM (2005)Google Scholar
  2. 2.
    Aggarwal, C.C., Li, N.: On node classification in dynamic content-based networks. In: SDM, pp. 355–366. SIAM (2011)Google Scholar
  3. 3.
    Baluja, S., Seth, R., Sivakumar, D., Jing, Y., Yagnik, J., Kumar, S., Ravichandran, D., Aly, M.: Video suggestion and discovery for youtube: taking random walks through the view graph. In: WWW, pp. 895–904. ACM (2008)Google Scholar
  4. 4.
    Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. The Journal of Machine Learning Research 7, 2399–2434 (2006)zbMATHMathSciNetGoogle Scholar
  5. 5.
    Chapelle, O., Schölkopf, B., Zien, A., et al.: Semi-supervised learning, vol. 2. MIT Press, Cambridge (2006)Google Scholar
  6. 6.
    Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: SIGCOMM, pp. 251–262 (1999)Google Scholar
  7. 7.
    Fang, Y., Hsu, B.-J.P., Chang, K.C.-C.: Confidence-aware graph regularization with heterogeneous pairwise features. In: SIGIR, pp. 951–960. ACM (2012)Google Scholar
  8. 8.
    Gong, C., Tao, D., Fu, K., Yang, J.: Relish: Reliable label inference via smoothness hypothesis. In: AAAI (2014)Google Scholar
  9. 9.
    McGlohon, M., Bay, S., Anderle, M.G., Steier, D.M., Faloutsos, C.: Snare: a link analytic system for graph labeling and risk detection. In: KDD, pp. 1265–1274. ACM (2009)Google Scholar
  10. 10.
    Mislove, A., Viswanath, B., Gummadi, K.P., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: WSDM, pp. 251–260. ACM (2010)Google Scholar
  11. 11.
    Orbach, M., Crammer, K.: Graph-based transduction with confidence. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012, Part II. LNCS, vol. 7524, pp. 323–338. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  12. 12.
    Sun, Y., Han, J., Gao, J., Yu, Y.: itopicmodel: Information network-integrated topic modeling. In: ICDM, pp. 493–502. IEEE (2009)Google Scholar
  13. 13.
    Takac, L., Zabovsky, M.: Data analysis in public social networks. In: International Scientific Conference and International Workshop Present Day Trends of Innovations (2012)Google Scholar
  14. 14.
    Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. Advances in Neural Information Processing Systems 16(16), 321–328 (2004)Google Scholar
  15. 15.
    Zhu, X., Ghahramani, Z., Lafferty, J., et al.: Semi-supervised learning using gaussian fields and harmonic functions. In: ICML, vol. 3, pp. 912–919 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yuto Yamaguchi
    • 1
  • Christos Faloutsos
    • 2
  • Hiroyuki Kitagawa
    • 1
  1. 1.University of TsukubaTsukubaJapan
  2. 2.Carnegie Mellon UniversityPittsburghUSA

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