Graph neural networks have emerged as a leading paradigm for inferring node labels in complex relational data. By extending convolutional and attention operations to arbitrary graph structures, these ...

Abstract: With the explosive growth of graph data in scale, noise, and structural complexity, existing graph neural networks (GNNs) are reaching a performance bottleneck when simultaneously modelling ...

Abstract: Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriad graph analytic tasks and applications. Most GNNs rely on the homophily assumption ...

#include "core/io/config_file.h" #include "core/io/file_access.h" #include "core/io/image.h" #include "core/io/resource_importer.h" #include "core/io/resource_loader ...

There's a cambrian explosion of image, video, audio, and text models happening right now — every week brings a new provider with a new endpoint. Stitching them together today means writing throwaway ...