Graph Isomorphism Network (GIN)¶
Graph Isomorphism Network (GIN) is a simple graph neural network that expects to achieve the ability as the Weisfeiler-Lehman graph isomorphism test. Based on PGL, we reproduce the GIN model.
Datasets¶
The dataset can be downloaded from here.
After downloading the data，uncompress them, then a directory named ./dataset/ can be found in current directory. Note that the current directory is the root directory of GIN model.
Dependencies¶
paddlepaddle >= 2.0.0
pgl >= 2.0
How to run¶
For examples, use GPU to train GIN model on MUTAG dataset.
export CUDA_VISIBLE_DEVICES=0
python main.py --use_cuda --dataset_name MUTAG --data_path ./dataset
Hyperparameters¶
data_path: the root path of your dataset
dataset_name: the name of the dataset
fold_idx: The \(fold\_idx^{th}\) fold of dataset splited. Here we use 10 fold cross-validation
train_eps: whether the \(\epsilon\) parameter is learnable.
Experiment results （Accuracy）¶
MUTAG |
COLLAB |
IMDBBINARY |
IMDBMULTI |
|
|---|---|---|---|---|
PGL result |
90.8 |
78.6 |
76.8 |
50.8 |
paper reuslt |
90.0 |
80.0 |
75.1 |
52.3 |