In bibliometrics, and citation analysis studies in particular,
many attempts have been made to analyze 
the relationship among scientific papers, authors and journals.
Recently, 
these research results are found to be effective for
analyzing the link structure of www pages as well.
We explore the application of kernel methods to citation analysis.
Kernel-based machine learning methods are getting popular
due to its power and its ability to
deal with (semi-)structured data such as strings and graphs.
We show that a family of kernels on graphs
provides a unified perspective 
on the three bibliometric measures
that have been discussed independently:
relatedness between documents,
overall importance of individual documents, and
importance of documents relative to one or more (root) documents
(relative importance).
The framework provided by the kernels
establishes relative importance as 
an intermediate between relatedness and overall importance, 
in which the degree of `relativity,'
or the bias between relatedness and importance, 
is naturally controlled by a parameter.