Heterogeneous Graph Attention Network (HAN) with DGL
This is an attempt to implement HAN with DGL's latest APIs for heterogeneous graphs. The authors' implementation can be found here.
python main.py for reproducing HAN's work on their dataset.
python main.py --hetero for reproducing HAN's work on DGL's own dataset.
Reference performance numbers for the ACM dataset:
|micro f1 score||macro f1 score|
|Softmax regression (own dataset)||89.66||89.62|
|DGL (own dataset)||91.51||91.66|
We ran a softmax regression to check the easiness of our own dataset. HAN did show some improvements.