Top Tweets for #graphKernels
Luca Cosmo et al. propose in this work to extend the classic convolution operator to the graph domain by using #graphkernels, i.e. #kernelfunctions that compute inner product on graphs.
This defines a complete structural model without having to compute the input graph's embedding
Graph Kernel Neural Networks
https://t.co/NulUFvfvg3
by Luca Cosmo et al.
#ConvolutionalNeuralNetwork #ComputerScience
Today with @emblebi, we're launching the #AlphaFold Protein Structure Database, which offers the most complete and accurate picture of the human proteome, doubling humanity’s accumulated knowledge of high-accuracy human protein structures - for free: https://t.co/vtBGmTkKhy 1/
@lawrennd @deliprao Yes, that's why we have recently written up all we know about #graphkernels (and their link to #GNNs) in a long review (181 pages) in: https://t.co/6mdPTwVGui (free on arxiv: https://t.co/8vwiwWHbRM)
Officially published:
Karsten Borgwardt, Elisabetta Ghisu, Felipe Llinares-López, Leslie O'Bray and Bastian Rieck (2020)
"#GraphKernels: State-of-the-Art and Future Challenges",
Foundations and Trends® in #MachineLearning: Vol. 13: No. 5-6, pp 531-712.
https://t.co/YH0zQmdIeG
@mmbronstein I think in terms of theory and expressivity, a clear understanding of the similarities (even identities) and dissimilarities of #GraphNeuralNetworks and #GraphKernels would be desirable.
We have compiled a list of our papers on graph kernels (spanning 15 years of work) on https://t.co/fiC1eX4mKL @AGKBorgwardt #MachineLearning #Graphs #NetworkScience #graphkernels
@Montreal_AI Interesting, you can compute fractional automophisms to exploit symmetries when running message-passing or sampling based inference! This is what has been used for #graphKernels #liftedInference . @guyvdb @Mniepert
How can #MachineLearning help to #ReduceCosts and #DevelopmentTime when it comes to #chemistry? ⚗️
Find out here: https://t.co/PX4sUXYQyi
#GraphKernels #NeuralNetworkArchitectures #DataScience #research #researcher #DataScientistjobs #SciComm #PhDjobs #ChemicalCompounds
@brunofmr @jure We highlight the need for bigger datasets and standardized comparisons in an upcoming review on #GraphKernels. Great to see this initiative addressing it @Pseudomanifold @ElisabettaGhisu @FelipeLlinares @AGKBorgwardt
I fully agree. And while I rarely say this in public, it‘s a good opportunity to point out that everyone working on Graph Convolutional Networks should read about Graph Kernels as well, to understand where many of the key concepts come from. #GraphKernels #GCN #DeepLearning
It starts when follow-up work by well-known researchers doesn't admit that it's following your line. Next wave of papers propose "new" ideas, without realizing they are instances. Then work comes out where you're written out of the history.
Scholarship matters.
Congratulations to @matteotog @ElisabettaGhisu Felipe Llinares López @Pseudomanifold whose hard work was rewarded with a spotlight at #neurips 2019 for our paper on "Wasserstein Weisfeiler-Lehman Kernels on graphs", a new way to compare graphs. More details soon. #GraphKernels
#mdpientropy Can a #QuantumWalk Tell Which Is Which?A Study of Quantum Walk-Based Graph Similarity https://t.co/MKlUndMsge
#quantumwalks; #graphsimilarity; #graphkernels; #directedgraphs
#mobiUK the graph topology of an individual mobility network is often unique even after removing location and timestamp info from the nodes.
Quantifying privacy loss using graph kernel distance functions.
Dionysis Manousakas
#mobility #privacy #kanonymity #graphkernels
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