This DGL example implements the GNN model proposed in the paper MixHop: Higher-Order Graph Convolution Architectures via Sparsified Neighborhood Mixing. For the original implementation, see here.
Contributor: xnuohz
The codebase is implemented in Python 3.6. For version requirement of packages, see below.
dgl 0.5.2
numpy 1.19.4
pandas 1.1.4
tqdm 4.53.0
torch 1.7.0
The DGL's built-in Cora, Pubmed and Citeseer datasets. Dataset summary:
| Dataset | #Nodes | #Edges | #Feats | #Classes | #Train Nodes | #Val Nodes | #Test Nodes |
|---|---|---|---|---|---|---|---|
| Citeseer | 3,327 | 9,228 | 3,703 | 6 | 120 | 500 | 1000 |
| Cora | 2,708 | 10,556 | 1,433 | 7 | 140 | 500 | 1000 |
| Pubmed | 19,717 | 88,651 | 500 | 3 | 60 | 500 | 1000 |
--dataset str The graph dataset name. Default is 'Cora'.
--gpu int GPU index. Default is -1, using CPU.
--epochs int Number of training epochs. Default is 2000.
--early-stopping int Early stopping rounds. Default is 200.
--lr float Adam optimizer learning rate. Default is 0.5.
--lamb float L2 regularization coefficient. Default is 0.0005.
--step-size int Period of learning rate decay. Default is 40.
--gamma float Factor of learning rate decay. Default is 0.01.
--hid-dim int Hidden layer dimensionalities. Default is 60.
--num-layers int Number of GNN layers. Default is 4.
--input-dropout float Dropout applied at input layer. Default is 0.7.
--layer-dropout float Dropout applied at hidden layers. Default is 0.9.
--p list List of powers of adjacency matrix. Default is [0, 1, 2].
The following commands learn a neural network and predict on the test set. Training a MixHop model on the default dataset.
python main.pyTrain a model for 200 epochs and perform an early stop if the validation accuracy stops getting improved for 10 epochs.
python main.py --epochs 200 --early-stopping 10Train a model with a different learning rate and regularization coefficient.
python main.py --lr 0.001 --lamb 0.1Train a model with different model hyperparameters.
python main.py --num-layers 6 --p 2 4 6Train a model which follows the original hyperparameters on different datasets.
# Cora:
python main.py --gpu 0 --dataset Cora --lr 1 --input-dropout 0.6 --lamb 5e-3 --hid-dim 100 --num-layers 3
# Citeseer:
python main.py --gpu 0 --dataset Citeseer --lr 0.25 --input-dropout 0.5 --lamb 5e-3 --hid-dim 60 --num-layers 3
# Pubmed:
python main.py --gpu 0 --dataset Pubmed --lr 0.5 --input-dropout 0.7 --lamb 5e-3 --hid-dim 60 --num-layers 3| Dataset | Cora | Pubmed | Citeseer |
|---|---|---|---|
| Accuracy(MixHop: default architecture in Table 1) | 0.818 | 0.800 | 0.714 |
| Accuracy(official code) | 0.610(0.156) | 0.746(0.065) | 0.700(0.017) |
| Accuracy(DGL) | 0.801(0.005) | 0.780(0.005) | 0.692(0.005) |