GAM

A PyTorch implementation of Graph Classification Using Structural Attention (KDD 2018).

Abstract

This repository provides an implementation for GAM as described in the paper:

Graph Classification using Structural Attention. John Boaz Lee, Ryan Rossi, and Xiangnan Kong KDD, 2018. [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
argparse           1.1.0
sklearn            0.20.0
torch              1.2.0.
torchvision        0.3.0

Datasets

For example these JSON files have the following key-value structure:

{"target": 1,
 "edges": [[0, 1], [0, 4], [1, 3], [1, 4], [2, 3], [2, 4], [3, 4]],
 "labels": {"0": 2, "1": 3, "2": 2, "3": 3, "4": 4},
 "inverse_labels": {"2": [0, 2], "3": [1, 3], "4": [4]}}

Options

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

Input and output options

  --train-graph-folder   STR    Training graphs folder.      Default is `input/train/`.
  --test-graph-folder    STR    Testing graphs folder.       Default is `input/test/`.
  --prediction-path      STR    Path to store labels.        Default is `output/erdos_predictions.csv`.
  --log-path             STR    Log json path.               Default is `logs/erdos_gam_logs.json`. 

Model options

  --repetitions          INT         Number of scoring runs.                  Default is 10. 
  --batch-size           INT         Number of graphs processed per batch.    Default is 32. 
  --time                 INT         Time budget.                             Default is 20. 
  --step-dimensions      INT         Neurons in step layer.                   Default is 32. 
  --combined-dimensions  INT         Neurons in shared layer.                 Default is 64. 
  --epochs               INT         Number of GAM training epochs.           Default is 10. 
  --learning-rate        FLOAT       Learning rate.                           Default is 0.001.
  --gamma                FLOAT       Discount rate.                           Default is 0.99. 
  --weight-decay         FLOAT       Weight decay.                            Default is 10^-5. 

Examples

Training a GAM model on the default dataset. Saving predictions and logs at default paths.

python src/main.py

Training a GAM model for a 100 epochs with a batch size of 512.

python src/main.py --epochs 100 --batch-size 512

Setting a high time budget for the agent.

python src/main.py --time 128

Training a model with some custom learning rate and epoch number.

python src/main.py --learning-rate 0.001 --epochs 200

License

• GNU License