FastNoise2 is built around a node graph architecture. Rather than calling standalone functions to generate noise, you build a tree of interconnected nodes, then evaluate the root node to get the final ...
Abstract: Self-supervised graph embedding has emerged as a powerful paradigm for learning expressive node and graph representations without relying on real labels. Several recent self-supervised ...
Abstract: We investigate graph convolution networks with efficient learning from higher-order graph convolutions and direct learning from adjacency matrices for node classification. We revisit the ...
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us ...
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