This is the code for our NeurIPS 2024 paper Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers.

Python == 3.8

Pytorch == 1.11

dgl == 0.9

CUDA == 10.2

You can run each command in "Solo.sh".

You could change the hyper-parameters of GCFormer if necessary.

Due to the space limitation, please refer to this link to download the datasets as well as pre-computing data. Once you have done this, please put them into the corresponding folders (dataset, pre_features and pre_sample)

If you find this code useful, please consider citing the original work by authors:

@inproceedings{gcformer, 
author = {Jinsong Chen and Hanpeng Liu and John E. Hopcroft and Kun He},
 title = {Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers}, 
 booktitle = {Proceedings of the 38th Annual Conference on Neural Information Processing Systems},
 volume = {37},
 pages = {85824--85845}, 
 year = {2024} }