hpn

Heterogeneous Graph Propagation Network (HPN)

This is an implementation of HPN for heterogeneous graphs.

• Paper link: https://ieeexplore.ieee.org/abstract/document/9428609

Usage

python hpn_trainer.py

Note: this scripts only support IMDB, which means command python hpn_trainer.py --dataset ACM will not run on ACM. If you want to test the performance of other datasets, you are suggested to make some modification of the trainer script.

Performance

Reference performance numbers for the IMDB dataset: (0.01, 200, 0.0001, 8, 0.8, 0.58178, 0.002811689883326394)

train test val = 400, 3478, 400, about 9% for trianing

Dataset	Our(tf)	Our(th)	Our(pd)
IMDB	58.05(±0.38)	57.23(±0.47)	57.75(±0.34)

TL_BACKEND=tensorflow python3 hpn_trainer.py --lr 0.01 --hidden_dim 512 --iter_K 1 --l2_coef 0.001  --drop_rate 0.4 --alpha 0.3
TL_BACKEND=torch python3 hpn_trainer.py --lr 0.01 --hidden_dim 512 --iter_K 1 --l2_coef 0.001  --drop_rate 0.4 --alpha 0.3
TL_BACKEND=paddle python3 hpn_trainer.py --lr 0.01 --hidden_dim 512 --iter_K 1 --l2_coef 0.001  --drop_rate 0.4 --alpha 0.3