ARMA-dgl
ARMA-dgl copied to clipboard
xnuohz
A DGL implementation of "Graph Neural Networks with convolutional ARMA filters". (PAMI 2021)
DGL Implementation of ARMA
This DGL example implements the GNN model proposed in the paper Graph Neural Networks with convolutional ARMA filters. For the original implementation, see here.
Contributor: xnuohz
Requirements
The codebase is implemented in Python 3.6. For version requirement of packages, see below.
dgl 0.6a210202
numpy 1.19.5
networkx 2.5
scikit-learn 0.24.1
tqdm 4.56.0
torch 1.7.0s
The graph datasets used in this example
Node Classification
The DGL's built-in Cora, Pubmed, Citeseer and PPI datasets. Dataset summary:
| Dataset | #Nodes | #Edges | #Feats | #Classes | #Train Nodes | #Val Nodes | #Test Nodes |
|---|---|---|---|---|---|---|---|
| Cora | 2,708 | 10,556 | 1,433 | 7(single label) | 140 | 500 | 1000 |
| Citeseer | 3,327 | 9,228 | 3,703 | 6(single label) | 120 | 500 | 1000 |
| Pubmed | 19,717 | 88,651 | 500 | 3(single label) | 60 | 500 | 1000 |
| PPI | 56,944 | 818,716 | 50 | 121(multi label) | 44906(20 graphs) | 6514(2 graphs) | 5524(2 graphs) |
Graph Classification
| Dataset | #Samples | #Classes | #Avg. nodes | #Avg. edges | #Node attr. | Node labels |
|---|---|---|---|---|---|---|
| Enzymes | 600 | 6 | 32.63 | 62.14 | 18 | no |
| Proteins | 1,113 | 2 | 39.06 | 72.82 | 1 | no |
| D&D | 1,178 | 2 | 284.32 | 715.66 | - | yes |
| MUTAG | 188 | 2 | 17.93 | 19.79 | - | yes |
Usage
Dataset options
--dataset str The graph dataset name. Default is 'Cora'.
GPU options
--gpu int GPU index. Default is -1, using CPU.
Model options
--epochs int Number of training epochs. Default is 2000.
--early-stopping int Early stopping rounds. Default is 100.
--lr float Adam optimizer learning rate. Default is 0.01.
--lamb float L2 regularization coefficient. Default is 0.0005.
--hid-dim int Hidden layer dimensionalities. Default is 16.
--num-stacks int Number of K. Default is 2.
--num-layers int Number of T. Default is 1.
--dropout float Dropout applied at all layers. Default is 0.75.
Examples
The following commands learn a neural network and predict on the test set. Train an ARMA model which follows the original hyperparameters on different datasets.
# Cora:
python citation.py --gpu 0
# Citeseer:
python citation.py --gpu 0 --dataset Citeseer --num-stacks 3
# Pubmed:
python citation.py --gpu 0 --dataset Pubmed --dropout 0.25 --num-stacks 1
# PPI:
python ppi.py --gpu 0
# Enzymes
python tu.py --gpu 0
# Proteins
python tu.py --gpu 0 --dataset PROTEINS --num-stacks 4 --num-layers 4
# D&D
python tu.py --gpu 0 --dataset DD --dropout 0 --num-stacks 4 --num-layers 4
# MUTAG
python tu.py --gpu 0 --dataset MUTAG --dropout 0 --num-stacks 4 --num-layers 4
Performance
Node Classification
| Dataset | Cora | Citeseer | Pubmed | PPI |
|---|---|---|---|---|
| Metrics(Table 1.Node classification accuracy/f1) | 83.4±0.6 | 72.5±0.4 | 78.9±0.3 | 90.5±0.3 |
| Metrics(PyG) | 82.3±0.5 | 70.9±1.1 | 78.3±0.8 | - |
| Metrics(DGL) | 80.9±0.6 | 71.6±0.8 | 75.0±4.2 | 73.2±0.1 |
Graph Classification
| Dataset | Enzymes | Proteins | D&D | MUTAG |
|---|---|---|---|---|
| Metrics(Table 3.Graph classification accuracy.) | 60.6±7.2 | 73.7±3.4 | 77.6±2.7 | 91.5±4.2 |
| Metrics(DGL) | 98.2±2.2 | 100.0±0.0 | 100.0±0.0 | 13.0±26 |
xnuohz