Graph Kernels: State-of-the-Art and Future Challenges

@article{Borgwardt2020GraphKS,
  title={Graph Kernels: State-of-the-Art and Future Challenges},
  author={K. Borgwardt and Elisabetta Ghisu and F. Llinares-L{\'o}pez and Leslie O'Bray and Bastian Alexander Rieck},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.03854}
}
  • K. Borgwardt, Elisabetta Ghisu, +2 authors Bastian Alexander Rieck
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • Graph-structured data are an integral part of many application domains, including chemoinformatics, computational biology, neuroimaging, and social network analysis. Over the last two decades, numerous graph kernels, i.e. kernel functions between graphs, have been proposed to solve the problem of assessing the similarity between graphs, thereby making it possible to perform predictions in both classification and regression settings. This manuscript provides a review of existing graph kernels… CONTINUE READING

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