HAN

HAN code

Adapted from dgl/han.

We add GCN and GAT comparison under --hetero setting.

running environment

• Python 3.8.5
• torch 1.4.0 cuda 10.1
• dgl 0.5.2 cuda 10.1

running procedure

• Download ACM.mat from tsinghua-cloud or google-drive
• Move ACM.dat to the current directory
• run main.py

python main.py --model gcn
python main.py --model gat
python main.py --model han

performance report

	micro f1 score	macro f1 score
Softmax regression	89.66	89.62
HAN	91.90	91.95
GCN	92.79	92.87
GAT	92.83	92.86

The following content is from the initial dgl/han repo.

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.

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.