As new graph structured data is constantly being generated, learning and data mining on
graphs have become a challenge in application areas such as molecular biology …

The problem of accurately measuring the similarity between graphs is at the core of many
applications in a variety of disciplines. Graph kernels have recently emerged as a promising …

We present a unified framework to study graph kernels, special cases of which include the
random walk (Gärtner et al., 2003; Borgwardt et al., 2005) and marginalized (Kashima et al …

Graph classification is an increasingly important step in numerous application domains,
such as function prediction of molecules and proteins, computerised scene analysis, and …

Graph-structured data are an integral part of many application domains, including
chemoinformatics, computational biology, neuroimaging, and social network analysis. Over …

Graph-structured data arise in wide applications, such as computer vision, bioinformatics,
and social networks. Quantifying similarities among graphs is a fundamental problem. In this …

Graph kernels have attracted a lot of attention during the last decade, and have evolved into
a rapidly developing branch of learning on structured data. During the past 20 years, the …

In this article, we propose a family of efficient kernels for large graphs with discrete node
labels. Key to our method is a rapid feature extraction scheme based on the Weisfeiler …

Random walk graph kernel has been used as an important tool for various data mining tasks
including classification and similarity computation. Despite its usefulness, however, it suffers …

Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured
data, which is omnipresent in computational biology. Graph kernels have been proposed as …