DGL Implementation of MixHop

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

Requirements

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 graph datasets used in this example

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

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 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].

Examples

The following commands learn a neural network and predict on the test set. Training a MixHop model on the default dataset.

python main.py

Train 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 10

Train a model with a different learning rate and regularization coefficient.

python main.py --lr 0.001 --lamb 0.1

Train a model with different model hyperparameters.

python main.py --num-layers 6 --p 2 4 6

Train 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

Performance

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)