Scalable kernels for graphs with continuos attributes

Main content

Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten Borgwardt

Scalable kernels for graphs with continuos attributes

Code and Datasets

The files are available at our GitHub repository here. 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]

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Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne and Karsten Borgwardt
Scalable kernels for graphs with continuous attributes,
Advances in Neural Information Processing Systems 26 (NIPS 2013), 216-224. (Online)

Further information and the code can be found on the project page.

@incollection{Feragen-2013-NIPS,
title = {Scalable kernels for graphs with continuous attributes},
author = {Feragen, Aasa and Kasenburg, Niklas and Petersen, Jens and de Bruijne, Marleen and Borgwardt, Karsten},
booktitle = {Advances in Neural Information Processing Systems 26},
editor = {C. J. C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K. Q. Weinberger},
pages = {216--224},
year = {2013},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/5155-scalable-kernels-for-graphs-with-continuous-attributes.pdf}
}

[2]

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M. Neumann, N. Patricia, R. Garnett, and K. Kersting.
Efficient graph kernels by randomization.
ECML/PKDD (1), pages 378–393, 2012 (Online)

@Inbook{Neumann2012,
author="Neumann, Marion
and Patricia, Novi
and Garnett, Roman
and Kersting, Kristian",
editor="Flach, Peter A.
and De Bie, Tijl
and Cristianini, Nello",
title="Efficient Graph Kernels by Randomization",
bookTitle="Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part I",
year="2012",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="378--393",
isbn="978-3-642-33460-3",
doi="10.1007/978-3-642-33460-3_30",
url="http://dx.doi.org/10.1007/978-3-642-33460-3_30"
}

 
 
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