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Directed Laplacian Kernels for Link Analysis

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Intelligent Data Engineering and Automated Learning – IDEAL 2006 (IDEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4224))

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Abstract

Application of kernel methods to link analysis is presented. Novel kernels based on directed graph Laplacians are proposed and their application as measures of relatedness between nodes in a directed graph is presented. The kernels express relatedness and take into account the global importance of the nodes in a citation graph. Limitations of existing kernels are given with a discussion how they are addressed by directed Laplacian kernels. Links between the kernels and PageRank ranking algorithm are also presented. The proposed kernels are evaluated on a dataset of scientific bibliographic citations.

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References

  • Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46, 604–632 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  • Brin, S., Page, L., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the Web. Technical Report 1999-0120, Computer Science Department, Stanford University (1999)

    Google Scholar 

  • Ito, T., Shimbo, M., Kudo, T., Matsumoto, Y.: Application of kernels to link analysis. In: Proceedings of KDD, pp. 586–592 (2005)

    Google Scholar 

  • White, S., Smyth, P.: Algorithms for estimating relative importance in networks. In: Proceedings of SIGKDD, pp. 266–275 (2003)

    Google Scholar 

  • Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2001)

    Google Scholar 

  • Kandola, J., Shawe-Taylor, J., Cristianini, N.: Learning semantic similarity. In: NIPS 15, MA, pp. 657–664. MIT Press, Cambridge (2003)

    Google Scholar 

  • Kondor, R.I., Lafferty, J.: Diffusion kernels on graphs and other discrete structures. In: Proceedings of MGTS at ECML/PKDD, pp. 315–322 (2002)

    Google Scholar 

  • Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)

    Google Scholar 

  • Smola, A.J., Kondor, R.: Kernels and Regularization on Graphs. In: Proceedings of Conference on Learning Theory, COLT/KW (2003)

    Google Scholar 

  • Chung, F.R.K.: Laplacians and the Cheeger inequality for directed graphs. Annals of Combinatorics 9, 1–19 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  • Langville, A.N., Meyer, C.D.: Deeper inside PageRank. Internet Mathematics 1, 335–380 (2005)

    Article  MathSciNet  Google Scholar 

  • McCallum, A.K., Nigam, K., Rennie, J., Seymore, K.: Automating the construction of internet portals with machine learning. Information Retrieval 3, 127–163 (2000)

    Article  Google Scholar 

  • Fagin, R., Kumar, R., Sivakumar, D.: Comparing top k lists. SIAM Journal of Discrete Mathematics 17, 134–160 (2003)

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Gdańsk University of Technology, Narutowicza 11/12, 80-952, Gdańsk, Poland

    Pawel Majewski

Authors
  1. Pawel Majewski
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Editor information

Editors and Affiliations

  1. Escuela Politécnica Superior, GICAP Research Group, Universidad de Burgo, Calle Francisco de Vitoria S/N, Edifico C, Campus Vena, 09006, Burgos, Spain

    Emilio Corchado

  2. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  3. Department of Information Systems and Computation, Technical University of Valencia, Camino de Vera, Valencia, Spain

    Vicente Botti

  4. University of West Scotland, PA1 2BE, Paisley, Scotland

    Colin Fyfe

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© 2006 Springer-Verlag Berlin Heidelberg

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Majewski, P. (2006). Directed Laplacian Kernels for Link Analysis. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_38

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  • DOI: https://doi.org/10.1007/11875581_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45485-4

  • Online ISBN: 978-3-540-45487-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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