RSHN[ICDM2019]
How to run
-
Clone the Openhgnn-DGL
python main.py -m RSHN -t node_classification -d aifb -g 0
If you do not have gpu, set -gpu -1.
Supported Dataset: RDFDataset
Performance: Node classification
Method |
AIFB |
MUTAG |
BGS |
AM |
RSHN |
97.22 |
82.35 |
93.10 |
90.40 |
RSHN(openhgnn) |
97.22 |
85.29 |
93.10 |
89.39 |
The experiments in RSHN have serious problems according to the official code. First, it does not use validation set, and just tune hyperparameters on test set. Second, it reports the accuracy at the epoch with best accuracy on test set in the paper. And in this setting, we give our performance.
TrainerFlow: [node classification flow](../../trainerflow/#Node classification flow)
Model
- 1) Coarsened Line Graph Neural Network (CL-GNN):
- 2) Heterogeneous Graph Neural Network (H-GNN):
Hyper-parameter specific to the model
You can modify the parameters in openhgnn/config.ini
Description
# The next two hyper-parameters are used in building the coarsened-line graph.
rw_len = 5
batch_size = 1000
# edga_layer means number of CL-GNN layers, node_layer means number of H-GNN layers
num_node_layer = 2
num_edge_layer = 1
Best config can be found in best_config
More
Contirbutor
Tianyu Zhao[GAMMA LAB]
If you have any questions,
Submit an issue or email to tyzhao@bupt.edu.cn.