KDD 2005
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Slides used in the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. http://www.sigkdd.org/kdd2005/
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KDD 2005
- 1. Application of Kernels to Link Analysis Takahiko Ito † Masashi Shimbo † Taku Kudo ‡ Yuji Matsumoto † † Nara Institute of Science and Technology ‡ Google
Notes de l'éditeur
- First I will talk about the background of this work. Link analysis is an essential tool for exploring networked data. For example, PageRank and HITS evaluate the importance of web pages. Co-citation coupling used by CiteSeer to estimate relatedness between papers.
- On the other hand, several kernels for graphs have been proposed recently. In particular, a family of "diffusion" kernels defines an inner product of nodes in a graph, This family includes Heat kernels, Neumann kernels and regularized Laplacian kernels. Here, we have one question. What do these “inner products” represents when viewed as link analysis measures.
- In this work, We give an interpretation of some diffusion kernels in terms of link analysis. More specifically, we show the interpretation of Neumann kernels and regularized Laplaian kernels. First We show Neumann kernels provide a unified perspective of relatedness and importance. Then We show regularized Laplacian kernels define a new relatedness measure that overcomes some limitations of traditional relatedness. This topic is discussed later.
- In this work, We give an interpretation of some diffusion kernels in terms of link analysis. More specifically, we show the interpretation of Neumann kernels and regularized Laplaian kernels. First We show Neumann kernels provide a unified perspective of relatedness and importance. Then We show regularized Laplacian kernels define a new relatedness measure that overcomes some limitations of traditional relatedness. This topic is discussed later.
- In this work, We give an interpretation of some diffusion kernels in terms of link analysis. More specifically, we show the interpretation of Neumann kernels and regularized Laplaian kernels. First We show Neumann kernels provide a unified perspective of relatedness and importance. Then We show regularized Laplacian kernels define a new relatedness measure that overcomes some limitations of traditional relatedness. This topic is discussed later.