README

Overview

This project benchmarks graph neural networks (GNNs) on the OpenCL DevMap dataset. It supports multiple GNNs, bidirectional message passing, and directed/undirected graphs.

Key features:

• 10-fold stratified cross-validation
• Training with early stopping
• Support for fine-tuning pretrained autoencoders
• Logging to Weights & Biases (wandb)
• Evaluation with F1-score and ROC-AUC

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Usage

python train.py 

Refer to the arguments below when executing train.py

Argument	Type	Default	Description
--num_threads	int	2	Number of CPU threads to use.
--batch_size	int	2
--epochs	int	10
--patience	int	10	Early stopping patience.
--undirected	flag	False	If true, treat graphs as undirected (add reverse edges).
--bidirectional	flag	False	If true, use bidirectional GNN variant.
--wandb	flag	False	Enable logging to Weights & Biases.
--fine_tuning	flag	False	Use a pretrained autoencoder.
--GNN	str	"SAGE"	GNN options: SAGE or GIN.
--num_layers	int	1	Number of GNN layers.
--log_file_name	str	"default.txt"	File to log results.
--pretrained_model	str	"default.txt"	Path to pretrained model (if fine-tuning).
--seed	int	1	Random seed for reproducibility.

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Output

• Prints F1 score and ROC-AUC for train, validation, and test sets per fold.
• Logs results in the specified --log_file_name with columns:
• If --wandb is enabled, results are also logged to your wandb dashboard.

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