Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years. Most existing GCNNs are either spatial approaches working on neighborhood of each node, or ...

Existing GNNs usually conduct the layer-wise message propagation via the ‘full’ aggregation of all neighborhood information which are usually sensitive to the structural noises existed in the graphs, ...

ONNX provides a C++ library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes. The primary motivation is to share work between the ...