dgl/examples/pytorch/hgt

• Heterogeneous Graph Transformer (HGT)

Heterogeneous Graph Transformer (HGT)

Alternative PyTorch-Geometric implementation

“Heterogeneous Graph Transformer” is a graph neural network architecture that can deal with large-scale heterogeneous and dynamic graphs.

This toy experiment is based on DGL's official tutorial. As the ACM datasets doesn't have input feature, we simply randomly assign features for each node. Such process can be simply replaced by any prepared features.

The reference performance against R-GCN and MLP running 5 times:

Model	Test Accuracy	# Parameter
2-layer HGT	0.465 ± 0.007	2,176,324
2-layer RGCN	0.392 ± 0.013	416,340
MLP	0.132 ± 0.003	200,974