https://github.com/willyfh/graph-transformer

An unofficial implementation of Graph Transformer (Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification) - IJCAI 2021
https://github.com/willyfh/graph-transformer

deep-learning gnn graph-deep-learning graph-neural-networks graph-transformer neural-networks pytorch

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An unofficial implementation of Graph Transformer (Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification) - IJCAI 2021

• Host: GitHub
• URL: https://github.com/willyfh/graph-transformer
• Owner: willyfh
• License: mit
• Created: 2023-01-17T10:13:47.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-04-20T11:07:38.000Z (7 months ago)
• Last Synced: 2024-04-20T11:32:36.378Z (7 months ago)
• Topics: deep-learning, gnn, graph-deep-learning, graph-neural-networks, graph-transformer, neural-networks, pytorch
• Language: Python
• Homepage: https://www.ijcai.org/proceedings/2021/0214.pdf
• Size: 483 KB
• Stars: 22
• Watchers: 2
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# Graph Transformer (IJCAI 2021)

[![python](https://img.shields.io/badge/python-3.8%2B-blue)]() [![pytorch](https://img.shields.io/badge/pytorch-1.6%2B-orange)]() [![Downloads](https://static.pepy.tech/personalized-badge/graph-transformer?period=total&units=international_system&left_color=grey&right_color=green&left_text=PyPI%20Downloads)](https://pepy.tech/project/graph-transformer)

An unofficial implementation of Graph Transformer:


Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification) - IJCAI 2021 > https://www.ijcai.org/proceedings/2021/0214.pdf

This GNN architecture is implemented based on Section 3.1 (Graph Transformer) in the paper.

I implemented the code by referring to [this repository](https://github.com/lucidrains/graph-transformer-pytorch), but with some modifications to match with the original published paper in IJCAI 2021.

![image](https://github.com/willyfh/graph-transformer/blob/main/graph-transformer-architecture.png?raw=true)

## Installation

```bash

pip install graph-transformer

```

## Usage

```python

import torch

from graph_transformer import GraphTransformerModel

model = GraphTransformerModel(

        node_dim = 512,

        edge_dim = 512,

        num_blocks = 3, # number of graph transformer blocks

        num_heads = 8,

        last_average=True, # wether to average or concatenation at the last block

        model_dim=None # if None, node_dim will be used as the dimension of the graph transformer block

)

nodes = torch.randn(1, 128, 512)

edges = torch.randn(1, 128, 128, 512)

adjacency = torch.ones(1, 128, 128)

nodes = model(nodes, edges, adjacency)

```

**Note**: If your graph does not have edge features, you can set `edge_dim` and `edges` (in the forward pass) to `None`.