Distance Encoding for GNN Design

Last update: Nov 08, 2022

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Overview

Distance-encoding for GNN design

This repository is the official PyTorch implementation of the DEGNN and DEAGNN framework reported in the paper:
Distance-Encoding -- Design Provably More PowerfulGNNs for Structural Representation Learning, to appear in NeurIPS 2020.

The project's home page is: http://snap.stanford.edu/distance-encoding/

Authors & Contact

Pan Li, Yanbang Wang, Hongwei Wang, Jure Leskovec

Questions on this repo can be emailed to [email protected] (Yanbang Wang)

Installation

Requirements: Python >= 3.5, Anaconda3

Update conda:

conda update -n base -c defaults conda

Install basic dependencies to virtual environment and activate it:

conda env create -f environment.yml
conda activate degnn-env

Install PyTorch >= 1.4.0 and torch-geometric >= 1.5.0 (please refer to the PyTorch and PyTorch Geometric official websites for more details). Commands examples are:

conda install pytorch=1.4.0 torchvision cudatoolkit=10.1 -c pytorch
pip install torch-scatter==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
pip install torch-sparse==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
pip install torch-cluster==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
pip install torch-spline-conv==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
pip install torch-geometric

The latest tested combination is: Python 3.8.2 + Pytorch 1.4.0 + torch-geometric 1.5.0.

Quick Start

To train DEGNN-SPD Task 2 (link prediction) on C.elegans dataset:

python main.py --dataset celegans --feature sp --hidden_features 100 --prop_depth 1 --test_ratio 0.1 --epoch 300

This uses 100-dimensional hidden features, 80/10/10 split of train/val/test set, and trains for 300 epochs.

To train DEAGNN-SPD for Task 3 (node-triads prediction) on C.elegans dataset:

python main.py --dataset celegans_tri --hidden_features 100 --prop_depth 2 --epoch 300 --feature sp --max_sp 5 --l2 1e-3 --test_ratio 0.1 --seed 9

This enables 2-hop propagation per layer, truncates distance encoding at 5, and uses random seed 9.

To train DEGNN-LP (i.e. the random walk variant) for Task 1 (node-level prediction) on usa-airports using average accuracy as evaluation metric:

python main.py --dataset usa-airports --metric acc --hidden_features 100 --feature rw --rw_depth 2 --epoch 500 --bs 128 --test_ratio 0.1

Note that here the test_ratio currently contains both validation set and the actual test set, and will be changed to contain only test set.

To generate Figure2 LEFT of the paper (Simulation to validate Theorem 3.3):

python main.py --dataset simulation --max_sp 10

The result will be plot to ./simulation_results.png.

All detailed training logs can be found at <log_dir>/<dataset>/<training-time>.log. A one-line summary will also be appended to <log_dir>/result_summary.log for each training instance.

Usage Summary

Interface for DE-GNN framework [-h] [--dataset DATASET] [--test_ratio TEST_RATIO]
                                      [--model {DE-GNN,GIN,GCN,GraphSAGE,GAT}] [--layers LAYERS]
                                      [--hidden_features HIDDEN_FEATURES] [--metric {acc,auc}] [--seed SEED] [--gpu GPU]
                                      [--data_usage DATA_USAGE] [--directed DIRECTED] [--parallel] [--prop_depth PROP_DEPTH]
                                      [--use_degree USE_DEGREE] [--use_attributes USE_ATTRIBUTES] [--feature FEATURE]
                                      [--rw_depth RW_DEPTH] [--max_sp MAX_SP] [--epoch EPOCH] [--bs BS] [--lr LR]
                                      [--optimizer OPTIMIZER] [--l2 L2] [--dropout DROPOUT] [--k K] [--n [N [N ...]]]
                                      [--N N] [--T T] [--log_dir LOG_DIR] [--summary_file SUMMARY_FILE] [--debug]

Optinal Arguments

  -h, --help            show this help message and exit
  
  # general settings
  --dataset DATASET     dataset name
  --test_ratio TEST_RATIO
                        ratio of the test against whole
  --model {DE-GCN,GIN,GAT,GCN,GraphSAGE}
                        model to use
  --layers LAYERS       largest number of layers
  --hidden_features HIDDEN_FEATURES
                        hidden dimension
  --metric {acc,auc}    metric for evaluating performance
  --seed SEED           seed to initialize all the random modules
  --gpu GPU             gpu id
  --adj_norm {asym,sym,None}
                        how to normalize adj
  --data_usage DATA_USAGE
                        use partial dataset
  --directed DIRECTED   (Currently unavailable) whether to treat the graph as directed
  --parallel            (Currently unavailable) whether to use multi cpu cores to prepare data
  
  # positional encoding settings
  --prop_depth PROP_DEPTH
                        propagation depth (number of hops) for one layer
  --use_degree USE_DEGREE
                        whether to use node degree as the initial feature
  --use_attributes USE_ATTRIBUTES
                        whether to use node attributes as the initial feature
  --feature FEATURE     distance encoding category: shortest path or random walk (landing probabilities)
  --rw_depth RW_DEPTH   random walk steps
  --max_sp MAX_SP       maximum distance to be encoded for shortest path feature
  
  # training settings
  --epoch EPOCH         number of epochs to train
  --bs BS               minibatch size
  --lr LR               learning rate
  --optimizer OPTIMIZER
                        optimizer to use
  --l2 L2               l2 regularization weight
  --dropout DROPOUT     dropout rate
  
  # imulation settings (valid only when dataset == 'simulation')
  --k K                 node degree (k) or synthetic k-regular graph
  --n [N [N ...]]       a list of number of nodes in each connected k-regular subgraph
  --N N                 total number of nodes in simultation
  --T T                 largest number of layers to be tested
  
  # logging
  --log_dir LOG_DIR     log directory
  --summary_file SUMMARY_FILE
                        brief summary of training result
  --debug               whether to use debug mode

Reference

If you make use of the code/experiment of Distance-encoding in your work, please cite our paper:

@article{li2020distance,
  title={Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning},
  author={Li, Pan and Wang, Yanbang and Wang, Hongwei and Leskovec, Jure},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

Distance Encoding for GNN Design

Related tags

Overview

Distance-encoding for GNN design

Authors & Contact

Installation

Quick Start

Usage Summary

Optinal Arguments

Reference

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