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Application of Kernels to Link Analysis by Takahiko Ito; Masashi Shimbo; Taku Kudo; Yuji Matsumoto is a scholarly article available to read on EtoBox.

What is Application of Kernels to Link Analysis about?

The application of kernel methods to link analysis is explored. In particular, Kandola et al.'s Neumann kernels are shown to subsume not only the co-citation and bibliographic coupling relatedness but also Kleinberg's HITS importance. These popular measures of relatedness and importance correspond to the Neumann kernels at the extremes of their parameter range, and hence these kernels can be interpreted as defining a spectrum of link analysis measures intermediate between co-citation/bibliographic coupling and HITS. We also show that the kernels based on the graph Laplacian, including the regularized Laplacian and diffusion kernels, provide relatedness measures that overcome some limitations of co-citation relatedness. The property of these kernel-based link analysis measures is examined with a network of bibliographic citations. Practical issues in applying these methods to real data are discussed, and possible solutions are proposed.

Author

Takahiko Ito; Masashi Shimbo; Taku Kudo; Yuji Matsumoto

Publisher

ACM

Published

2005

Language

EN