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Found 2 papers, 1 papers with code
Covariant Compositional Networks For Learning Graphs
Most existing neural networks for learning graphs address permutation invariance by conceiving of the network as a message passing scheme, where each node sums the feature vectors coming from its neighbors.
The Multiscale Laplacian Graph Kernel
At the heart of the MLG construction is another new graph kernel, called the Feature Space Laplacian Graph kernel (FLG kernel), which has the property that it can lift a base kernel defined on the vertices of two graphs to a kernel between the graphs.
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