We introduce propagation kernels, a general graph-kernel framework for efficiently
measuring the similarity of structured data. Propagation kernels are based on monitoring …

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 …

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 …

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 …

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 …

Most state-of-the-art graph kernels only take local graph properties into account, ie, the
kernel is computed with regard to properties of the neighborhood of vertices or other small …

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 …

In the real world all events are connected. There is a hidden network of dependencies that
governs behavior of natural processes. Without much argument it can be said that, of all the …

Numerous important problems can be framed as learning from graph data. We propose a
framework for learning convolutional neural networks for arbitrary graphs. These graphs …

Current graph kernels suffer from two limitations: graph kernels based on counting particular
types of subgraphs ignore the relative position of these subgraphs to each other, while …