nslab-cuk / connector

The Graph Representation Learning Framework developed by NS Lab @ CUK.

Connector

Connector is a novel Graph Representation Learning Framework developed by NS Lab, CUK based on pure PyTorch backend.

Overview

We present Connector, a comprehensive graph representation learning framework developed primarily in Python using the PyTorch library. Connector Ver. 0.5 is a test release that will be further developed to enable researchers from different fields to apply graph representation learning models to their research. Connector consists of three main modules: graph data loaders, base model and graph representation learning modules. The graph loader modules introduce four graph loaders, namely homogeneous, heterogeneous, signed graph and knowledge graph loaders, which allow researchers to easily handle different types of graphs. By loading graph data, researchers can build and manipulate datasets with different file structures. The base model module includes different types of neural network layers, aggregators and sampling techniques for graphs to build common tasks such as loading models and loading/saving parameters.

Setup environment for running

• Running: python -m venv libenv

Connect to the environment

• On window OS run: env.bat
• On linux OS or MAC OS run: source image/bin/activate

Install pip packages

pip install -r requirements.txt

Inject the developed library

You should copy the graphrl folder and move it to libenv/Lib/site-packages to use this framework this time. Reseacher members will update this part after publishing this library

To build model

• Running python examples/TransE_example.py

Reference

📃 Paper on arXiv:

• 

📈 Experimental results on Papers With Code:

• 

📝 Blog on Network Science Lab:

• 

Citing Connector

Please cite our paper if you find Connector useful in your work:

@misc{nguyen2023connector,
      title={Connector 0.5: A unified framework for graph representation learning}, 
      author={Thanh Sang Nguyen and Jooho Lee and Van Thuy Hoang and O-Joun Lee},
      year={2023},
      eprint={2304.13195},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Contributors

---

---