PyTorch-Geometric Implementation of MarkovGNN: Graph Neural Networks on Markov Diffusion

Overview

MarkovGNN

This is the official PyTorch-Geometric implementation of MarkovGNN paper under the title "MarkovGNN: Graph Neural Networks on Markov Diffusion". This method uses different markov graphs in different layers of the GNN.

PDF is available in arXiv

System requirements

Users will need to install the following tools (CPU version).

PyTorch: 1.7.0
PyTorch-Geometric: 1.6.1
PyTorchSparse: 0.6.8
PyTorch Scatter: 2.0.5
PyTorch Cluster: 1.5.8
PyTorch Spline Conv: 1.2.0
NetworkX: 2.2
scikit-learn: 0.23.2
Matplotlib: 3.0.3

How to run

To use random seed disable the seed-fixing portion in the main.py file. A list of sample commands to run the MarkovGCN models.

python main.py --edgelist datasets/input2f/email.edgelist --label datasets/input2f/email.nodes.labels --eps 0.26 --epoch 200 --alpha 0.1 --nlayers 3 --lrate 0.01 --droprate 0.3 --markov_agg

python main.py --edgelist datasets/input2f/usaairports.edgelist --label datasets/input2f/usaairports.nodes.labels --oneindexed 1 --epoch 200 --alpha 1.0 --eps 0.09 --lrate 0.01 --nlayers 4 --normrow 0 --inflate 1.5 --markov_agg

python main.py --edgelist datasets/input2f/yeast.edgelist --label datasets/input2f/yeast.nodes.labels --oneindexed 1 --onelabeled 1 --eps 0.75 --epoch 200 --inflate 1.7 --lrate 0.01 --alpha 0.8 --droprate 0.1 --nlayers 3 

python main.py --edgelist datasets/input3f/squirrel_edges.txt --label datasets/input3f/squirrel_labels.txt --feature datasets/input3f/squirrel_features.txt --epoch 200 --eps 0.05 --droprate 0.25 --markov_agg --nlayers 6 --markov_agg

python main.py --edgelist datasets/input3f/chameleon_edges.txt --label datasets/input3f/chameleon_labels.txt --feature datasets/input3f/chameleon_features.txt --epoch 200 --alpha 0.8 --nlayers 3 --eps 0.2 --inflate 1.5 --droprate 0.5 --markov_agg

python main.py --edgelist datasets/input3f/chameleon_edges.txt --label datasets/input3f/chameleon_labels.txt --feature datasets/input3f/chameleon_features.txt --epoch 200 --alpha 0.2 --nlayers 2 --eps 0.06 --inflate 1.8 --droprate 0.7 --markov_agg

python main.py --eps 0.03 --droprate 0.85 --epoch 300 --alpha 0.05 --nlayers 2 --lrate 0.005 --inflate 1.8 --markov_agg

python main.py --eps 0.03 --droprate 0.85 --epoch 300 --alpha 0.05 --nlayers 2 --lrate 0.001 --inflate 3.5 --markov_agg --dataset Citeseer

python main.py --edgelist datasets/input3f/actor_edges.txt --label datasets/input3f/actor_labels.txt --feature datasets/input3f/actor_features.txt --epoch 200  --alpha 0.4 --markov_agg --nlayers 4

python main.py --edgelist datasets/input3f/actor_edges.txt --label datasets/input3f/actor_labels.txt --feature datasets/input3f/actor_features.txt --epoch 200  --alpha 0.2 --markov_agg --nlayers 3 --eps 0.3

To compare the results with respect to vanilla GCN, use the argument --use_gcn in the command line.

Parameters

There are several options to run the method which are outlined in the main.py file.

--markov_dense -> markov process uses dense matrix multiplication (sparse matrix multiplicaiton is the default option)
--markov_agg -> i-th layer uses a markov matrix from i-th iteration, this option with higher threshold will produce better runtime
--use_gcn -> run the vanilla GCN model.
  e.g., $ python main.py --edgelist datasets/input3f/actor_edges.txt --label datasets/input3f/actor_labels.txt --feature datasets/input3f/actor_features.txt --epoch 200  --use_gcn

Citation

If you find this repository helpful, please cite the following paper:

@article{rahman2022markovgnn,
  title={{MarkovGNN: Graph} Neural Networks on Markov Diffusion},
  author={Rahman, Md. Khaledur and Agrawal, Abhigya and Azad, Ariful},
  booktitle={arXiv preprint arXiv:2202.02470},
  year={2022}
}

Contact

Please create an issue if you face any problem to run this method. Don't hesitate to contact the following person if you have any questions: Md. Khaledur Rahman ([email protected]).

Owner
HipGraph: High-Performance Graph Analytics and Learning
HipGraph: High-Performance Graph Analytics and Learning
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