[KDD 2021, Research Track] DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks

Related tags

Deep Learning neural-architecture-search heterogeneous-information-networks graph-neural-networks
Overview

DiffMG

This repository contains the code for our KDD 2021 Research Track paper: DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks. https://arxiv.org/abs/2010.03250

Screenshot 2021-06-22 at 3 08 06 PM

Environment

python=3.8
pytorch==1.6.0 with CUDA support (by default our model is trained on GPU)
numpy==1.19.1
scipy==1.5.2
pandas==1.1.1
scikit-learn==0.23.2

How to run

For the node classification task, please see README under nc, and for the recommendation task, please see README under lp.

Citation

If you find our work helpful in your own research, please consider citing our paper:

@inproceedings{diffmg,
  title={DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks},
  author={Ding, Yuhui and Yao, Quanming and Zhao, Huan and Zhang, Tong},
  booktitle={Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2021}
}
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