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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.

Usage

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

Performance

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.