MGNNI: Multiscale Graph Neural Networks with Implicit Layers

The implementation of MGNNI: Multiscale Graph Neural Networks with Implicit Layers (NeurIPS 2022).

Requirements

The script has been tested running under Python 3.6.9, with the following packages installed (along with their dependencies):

• pytorch (tested on 1.6.0)
• torch_geometric (tested on 1.6.3)
• scipy (tested on 1.5.2)
• numpy (tested on 1.19.2)

Run Experiments

We provides some examples for running experiments for different tasks on different datasets:

Node classification

cd nodeclassification

For chameleon and squirrel datasets,

python train_MGNNI_heterophilic.py --dataset chameleon --lr 0.01 --weight_decay 5e-4 --model MGNNI_m_MLP --fp_layer MGNNI_m_att --batch_norm 1 --ks [1,2] --idx_split 0 --epoch 10000 --patience 500 

For Cornell, Texas, Wisconsin datasets,

python train_MGNNI_heterophilic.py --dataset cornell --lr 0.5 --weight_decay 5e-6 --model MGNNI_m_att --ks [1,2] --epoch 10000 --patience 500 --idx_split 0

idx_split should be changed accordingly. There are 10 data splits as used in Geom-GCN.

For PPI dataset,

python train_MGNNI_m_att_PPI.py --model MGNNI_m_att_stack --dropout 0.1 --epoch 5000 --hidden 2048 --ks [1,2]

Graph classification

cd graphclassification

python train_MGNNI_att.py --dataset MUTAG --lr 0.01 --weight_decay 0.0 --num_layers 3 --ks [1,2] --epochs 500 

This implementation is developed based on the original implementation of IGNN and EIGNN. We thank them for their useful implementation.

If you find our implementation useful in your research, please consider citing our paper:

@inproceedings{liu2022mgnni,
 author = {Liu, Juncheng and Hooi, Bryan and Kawaguchi, Kenji and Xiao, Xiaokui},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {MGNNI: Multiscale Graph Neural Networks with Implicit Layers},
 year = {2022}
}