Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Propagation kernels: Efficient graph kernels from propagated information
 Machine learning 102 (2016), Nr.2, S.209245 ISSN: 08856125 

 Englisch 
 Zeitschriftenaufsatz 
 Fraunhofer IAIS () 
Abstract
We introduce propagation kernels, a general graphkernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage earlystage distributions from propagation schemes such as random walks to capture structural information encoded in node labels, attributes, and edge information. This has two benefits. First, offtheshelf propagation schemes can be used to naturally construct kernels for many graph types, including labeled, partially labeled, unlabeled, directed, and attributed graphs. Second, by leveraging existing efficient and informative propagation schemes, propagation kernels can be considerably faster than stateoftheart approaches without sacrificing predictive performance. We will also show that if the graphs at hand have a regular structure, for instance when modeling image or video data, one can exploit this regularity to scale the kernel computation to large databases of graphs with thousands of nodes. We support our contributions by exhaustive experiments on a number of realworld graphs from a variety of application domains.