document: Application of kernels to link analysis 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 .
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