Scalable kernels for graphs with continuos attributes
Code and Datasets
The files are available at our GitHub repository external pagehere. They include
- a MATLAB implementation of the GraphHopper kernel as proposed in [1],
- an implementation of the propagation kernel as presented in [2], and
- the datasets used in the papers.
Publications
[1]
Scalable kernels for graphs with continuous attributes
Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne and Karsten Borgwardt
Advances in Neural Information Processing Systems 26 (NIPS 2013), 216-224
external pageOnline | ETH Research Collection | Project page | external pageGitHub
[2]
Efficient graph kernels by randomization
Marion Neumann, Novi Patricia, Roman Garnett and Kristian Kersting
Lecture Notes in Computer Science 7523 "Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012.", 378–393
external pageOnline