As Graph Neural Networks (GNNs) has become increasingly popular, there is a wide interest of designing deeper GNN architecture. However, deep GNNs suffer from the oversmoothing issue where the learnt...
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NYU Professor, Director of Facebook AI Lab
By far the cleanest and most elegant library for graph neural networks in PyTorch. Highly recommended! Unifies Capsule Nets (GNNs on bipartite graphs)
and Transformers (GCNs with attention on fully-connected graphs) in a single API.
Inventor of Graph Convolutional Network
I taught my students Deep Graph Library (DGL) in my lecture on "Graph Neural Networks" today. It is a great resource to develop GNNs with PyTorch.