Graph Wavelet Neural Network

A PyTorch implementation of Graph Wavelet Neural Network (ICLR 2019).

Abstract

A reference Tensorflow implementation is accessible [here].

This repository provides an implementation of Graph Wavelet Neural Network as described in the paper:

Graph Wavelet Neural Network. Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng. ICLR, 2019. [Paper]

Requirements

The codebase is implemented in Python 3.5.2. package versions used for development are just below.

networkx          2.4
tqdm              4.28.1
numpy             1.15.4
pandas            0.23.4
texttable         1.5.0
scipy             1.1.0
argparse          1.1.0
torch             1.1.0
torch-scatter     1.4.0
torch-sparse      0.4.3
torch-cluster     1.4.5
torch-geometric   1.3.2
torchvision       0.3.0
scikit-learn      0.20.0
PyGSP             0.5.1

Datasets

{ 0: [0, 1, 38, 1968, 2000, 52727],
  1: [10000, 20, 3],
  2: [],
  ...
  n: [2018, 10000]}

Options

Training the model is handled by the src/main.py script which provides the following command line arguments.

Input and output options

  --edge-path        STR   Input graph path.   Default is `input/cora_edges.csv`.
  --features-path    STR   Features path.      Default is `input/cora_features.json`.
  --target-path      STR   Target path.        Default is `input/cora_target.csv`.
  --log-path         STR   Log path.           Default is `logs/cora_logs.json`.

Model options

  --epochs                INT       Number of Adam epochs.         Default is 200.
  --learning-rate         FLOAT     Number of training epochs.     Default is 0.01.
  --weight-decay          FLOAT     Weight decay.                  Default is 5*10**-4.
  --filters               INT       Number of filters.             Default is 16.
  --dropout               FLOAT     Dropout probability.           Default is 0.5.
  --test-size             FLOAT     Test set ratio.                Default is 0.2.
  --seed                  INT       Random seeds.                  Default is 42.
  --approximation-order   INT       Chebyshev polynomial order.    Default is 3.
  --tolerance             FLOAT     Wavelet coefficient limit.     Default is 10**-4.
  --scale                 FLOAT     Heat kernel scale.             Default is 1.0.

Examples

python src/main.py

Training a model with more filters in the first layer.

python src/main.py --filters 32

Approximationg the wavelets with polynomials that have an order of 5.

python src/main.py --approximation-order 5

License

• GNU License