https://github.com/joisino/laf

Code for "Training-free Graph Neural Networks and the Power of Labels as Features" (TMLR 2024)
https://github.com/joisino/laf

deep-learning gnn graph-neural-networks label-propagation machine-learning node-classification pytorch transductive-learning

Last synced: about 4 hours ago
JSON representation

Code for "Training-free Graph Neural Networks and the Power of Labels as Features" (TMLR 2024)

• Host: GitHub
• URL: https://github.com/joisino/laf
• Owner: joisino
• License: mit
• Created: 2024-08-15T11:36:30.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-08-15T11:38:43.000Z (about 1 month ago)
• Last Synced: 2024-09-22T06:02:21.710Z (2 days ago)
• Topics: deep-learning, gnn, graph-neural-networks, label-propagation, machine-learning, node-classification, pytorch, transductive-learning
• Language: Python
• Homepage: https://arxiv.org/abs/2404.19288
• Size: 188 KB
• Stars: 39
• Watchers: 2
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Training-free Graph Neural Networks and the Power of Labels as Features (TMLR 2024)

[![arXiv](https://img.shields.io/badge/arXiv-2404.19288-b31b1b.svg)](https://arxiv.org/abs/2404.19288)

We propose training-free graph neural networks (TFGNNs), which can be used without training and can also be improved with optional training.

Paper: https://arxiv.org/abs/2404.19288

## 💿 Preparation

Install [Poetry](https://python-poetry.org/) and run the following command:

```bash

$ poetry install

```

## 🧪 Evaluation

### TFGNNs Outperform Existing GNNs in Training-free Setting

```bash

$ poetry run python trainingfree.py

```

| Model                               | Cora | CiteSeer | PubMed | CS   | Physics | Computers | Photo |

|-------------------------------------|------|----------|--------|------|---------|-----------|-------|

| GCNs                                | 0.163 | 0.167    | 0.180  | 0.079 | 0.101   | 0.023     | 0.119 |

| GCNs + LaF                          | 0.119 | 0.159    | 0.407  | 0.080 | 0.146   | 0.061     | 0.142 |

| GATs                                | 0.177 | 0.229    | 0.180  | 0.040 | 0.163   | 0.058     | 0.122 |

| GATs + LaF                          | 0.319 | 0.077    | 0.180  | 0.076 | 0.079   | 0.025     | 0.044 |

| TFGNNs + random initialization      | 0.149 | 0.177    | 0.180  | 0.023 | 0.166   | 0.158     | 0.090 |

| **TFGNNs (proposed)**               | **0.600** | **0.362**    | **0.413**  | **0.601** | **0.717**   | **0.730**     | **0.637** |

▲ **Results.** TFGNNs outperform existing GNNs in the training-free setting.

### Deep TFGNNs Perform Better in Training-free Setting

```bash

$ poetry run python numlayers.py

```



▲ **Results.** Deep TFGNNs perform better in the training-free setting.

### TFGNNs Converge Fast in Optional Training

```bash

$ poetry run python training.py

```



▲ **Results.** TFGNNs converge fast in optional training.

### TFGNNs are Robust to Feature Noise

```bash

$ poetry run python noisy.py

```



▲ **Results.** TFGNNs are robust to feature noise.

## 🖋️ Citation

```

@article{sato2024trainingfree,

  author    = {Ryoma Sato},

  title     = {Training-free Graph Neural Networks and the Power of Labels as Features},

  journal   = {Transactions on Machine Learning Research},

  year      = {2024},

}

```