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 from
here. The dataset is noisy
because there are same author occurring multiple times as different nodes.
For sampling-based training, python train_sampling.py
Reference performance numbers for the ACM dataset:
| micro f1 score | macro f1 score | |
|---|---|---|
| Paper | 89.22 | 89.40 |
| DGL | 88.99 | 89.02 |
| 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.