GraphCTA

Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation (WWW 2024)

This is a PyTorch implementation of the GraphCTA algorithm, which tries to address the domain adaptation problem without accessing the labelled source graph. It performs model adaptation and graph adaptation collaboratively through a series of procedures: (1) conduct model adaptation based on node's neighborhood predictions in target graph considering both local and global information; (2) perform graph adaptation by updating graph structure and node attributes via neighborhood constrastive learning; and (3) the updated graph serves as an input to facilitate the subsequent iteration of model adaptation, thereby establishing a collaborative loop between model adaptation and graph adaptation.

Requirements

• python3.8
• pytorch==2.0.0
• torch-scatter==2.1.1+pt20cu118
• torch-sparse==0.6.17+pt20cu118
• torch-cluster==1.6.1+pt20cu118
• torch-geometric==2.3.1
• numpy==1.24.3
• scipy==1.10.1
• tqdm==4.65.0

Datasets

Datasets used in the paper are all publicly available datasets. You can find Elliptic, Twitch and Citation via the links.

Quick Start:

Just execuate the following command for source model pre-training:

python train_source.py

Then, execuate the following command for adaptation:

python train_target.py

Citing

If you find GraphCTA useful for your research, please consider citing the following paper:

@inproceedings{zhang2024collaborate,
  title={Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation},
  author={Zhang, Zhen and Liu, Meihan and Wang, Anhui and Chen, Hongyang and Li, Zhao and Bu, Jiajun and He, Bingsheng},
  booktitle={Proceedings of the ACM on Web Conference 2024},
  pages={664--675},
  year={2024}
}