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

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elvis: Graph-structured data will continue to enable some of the most interesting applications in the field of machine learning -- ranging from social network analysis to neuroimaging. Check out this recent paper reviewing graph kernels and future challenges. https://arxiv.org/abs/2011.03854 https://t.co/oUw2w48hb1

2 replies, 419 likes


Karsten Borgwardt: Excited by the current boom in learning on graphs, we have reviewed the foundations and trends in #graphkernel research. We hope that it will be a reference and starting point for lots of future work in this domain! #MachineLearning #DeepLearning #Kernels https://arxiv.org/abs/2011.03854 https://t.co/Ssxfo5MfR5

3 replies, 405 likes


Bastian Rieck: Everything you ever wanted to know about graph kernels and more :)

1 replies, 42 likes


Dr ChloƩ Azencott: Oooh, lovely! I've been looking for an up-to-date review - often as a pointer for people who don't seem aware that learning on graphs didn't start with deep learning, because I'm a bitter old lady who wishes those kids would get off her lawn.

1 replies, 17 likes


Popular ML resources: The most popular ArXiv tweet in the last 24h: https://twitter.com/kmborgwardt/status/1326171326357319680

0 replies, 15 likes


Ioannis Xenarios: hmm reading for a few weekend

0 replies, 2 likes


Content

Found on Nov 10 2020 at https://arxiv.org/pdf/2011.03854.pdf

PDF content of a computer science paper: Graph Kernels: State-of-the-Art and Future Challenges