Abstract
Random walk plays a significant role in computer science. The popular PageRank algorithm uses random walk. Personalized random walks force random walk to “personalized views” of the graph according to users’ preferences. In this paper, we show the close relations between different preferential random walks and label propagation methods used in semi-supervised learning. We further present a maximum consistency algorithm on these preferential random walk/label propagation methods to ensure maximum consistency from labeled data to unlabeled data. Extensive experimental results on 9 datasets provide performance comparisons of different preferential random walks/label propagation methods. They also indicate that the proposed maximum consistency algorithm clearly improves the classification accuracy over existing methods.
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References
Pearson, K.: The problem of the Random Walk. Nature (1905)
Craswell, N., Szummer, M.: Random walks on the click graph. In: SIGIR (2007)
Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: WSDM (2011)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford University Database Group (1998)
Glen, J., Jennifer, W.: Scaling personalized web search. Technical report, Stanford University Database Group (2002)
Chapelle, O., Schölkopf, B., Zien, A.: Semi-Supervised Learning. MIT Press, Cambridge (2006)
Zhu, X.: Semi-supervised learning literature survey. Technical Report 1530, University of Wisconsin-Madison (2008)
Blum, A., Mitchell, T.M.: Combining labeled and unlabeled sata with co-training. In: COLT, pp. 92–100 (1998)
Joachims, T.: Transductive learning via spectral graph partitioning. In: ICML, pp. 290–297 (2003)
Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of the 20th International Conference on Machine Learning, pp. 912–919 (2003)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Scholkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems, vol. 16, pp. 321–328 (2004)
Ding, C.H.Q., Jin, R., Li, T., Simon, H.D.: A learning framework using green’s function and kernel regularization with application to recommender system. In: KDD, pp. 260–269 (2007)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems 16, pp. 321–328. MIT Press (2004)
Minkov, E., Cohen, W.W.: Learning to rank typed graph walks: Local and global approaches. In: WebKDD and SNA-KDD Joint Workshop (2007)
Tong, H., Faloutsos, C., Pan, J.-Y.: Fast Random Walk with Restart and Its Applications. In: ICDM (2006)
Tong, H., Faloutsos, C.: Center-Piece Subgraphs: Problem Definition and Fast solutions. In: KDD (2006)
Gallagher, B., Tong, H., Eliassi-Rad, T., Faloutsos, C.: Using ghost edges for classification in sparsely labeled networks. In: KDD (2008)
Koutra, D., Ke, T.-Y., Kang, U., Horng (Polo) Chau, D., Pao, H.-K.K., Faloutsos, C.: Unifying Guilt-by-Association Approaches: Theorems and Fast Algorithms. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS, vol. 6912, pp. 245–260. Springer, Heidelberg (2011)
Pearl, J.: Reverend bayes on inference engines: A distributed hierarchical approach. In: AAAI, pp. 133–136 (1982)
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Magazine 29, 93–106 (2008)
Yedidia, J.S., Freeman, W.T., Weiss, Y.: Constructing free energy approximations and generalized belief propagation algorithms. IEEE Transactions on Information Theory 51, 2282–2312 (2005)
Flake, G.W., Lawrence, S., Giles, C.L., Coetzee, F.M.: Self-Organization and Identification of Web Communities. IEEE Computer 35 (2002)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE, 2278–2324 (1998)
Dueck, D., Frey, B.J.: Non-metric affinity propagation for unsupervised image categorization. In: ICCV (2007)
Lee, Y.J., Grauman, K.: Foreground focus: Unsupervised learning from partially matching images. International Journal of Computer Vision 85, 143–166 (2009)
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Kong, D., Ding, C. (2012). Maximum Consistency Preferential Random Walks. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33486-3_22
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DOI: https://doi.org/10.1007/978-3-642-33486-3_22
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