This project focuses on tuning the hyperparameters of various Graph Neural Network (GNN) models and provides implementations for the APPNP, Splineconv, and GAT models using PyTorch Geometric.
-
Hyperparameter Optimization: Using Bayesian optimization provided by Optuna to find the best hyperparameters that lead to optimal model performance.
-
Model Implementations: Built-in support for APPNP, Splineconv, and GAT architectures. Each model has its own customizable parameters.
- Model-specific hyperparameter search space.
- Modular design for easy expansion to new GNN architectures.
- Pre-defined activation functions for flexibility.
- Comprehensive evaluation on graph datasets like Cora, PubMed, and CiteSeer.
- Python 3.x
- torch
- torch_geometric
- optuna
- Clone the repository:
git clone https://github.com/darkzard05/planetoid_search_hyperparam.git
- Install the requied packages:
pip install -r requirements.txt
- Run the main script with desired arguments:
python main.py --dataset=Cora --model==APPNP --n_trials=100 --epochs=100 --batch_size=1024 --num_neighbors=[10,10] --num_workers=0
- model: Model to be used. (APPNP, Splineconv, GAT)
- dataset: Dataset to be used. (Cora, PubMed, CiteSeer, Reddit)
- n_trials: Number of trials.
- epochs: Number of epochs per trial.
- batch_size: set data per iteration. (Reddit)
- num_neighbors: neighbors sampled in graph layer. (Reddit)
- num_workers: how many subprocesses to use for data loading. (default: 0)
- Implements the Approximated Personalized Propagation of Neural Predictions (APPNP) layer.
- Utilizes B-spline basis functions to hierarchically partition and transform the input graph.
- Graph Attention Networks (GAT) use attention mechanisms to weigh neighbor features.