TADA: Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks

Introduction

TADA, an efficient and effective front-mounted data augmentation framework for GNNs on HDGs. Under the hood, TADA includes two key modules: (i) feature expansion with structure embeddings, and (ii) topology- and attribute-aware graph sparsification. The former obtains augmented node features and enhanced model capacity by encoding the graph structure into high-quality structure embeddings with our highly-efficient sketching method. Further, by exploiting taskrelevant features extracted from graph structures and attributes, the second module enables the accurate identification and reduction of numerous redundant/noisy edges from the input graph, thereby alleviating over-smoothing and facilitating faster feature aggregations over HDGs. Empirically, TADA considerably improves the predictive performance of mainstream GNN models on 8 real homophilic/heterophilic HDGs in terms of node classification, while achieving efficient training and inference processes.

Environment settings

• python==3.11.5
• pytorch==2.0.1
• cuda==12.1
• torch_geometric==2.4.0

you can create and activate the environments by following code :

conda env create -f environments.yml
conda activate tada

Run

if you want to reproduce the results of GNNs and GNNs+TADA., please run the following command:

bash run.sh

Citation

Please kindly cite our work if you find our paper or codes helpful.

@inproceedings{lai2024efficient,
  title={Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks},
  author={Lai, Yurui and Lin, Xiaoyang and Yang, Renchi and Wang, Hongtao},
  booktitle={Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining},
  pages={1463--1473},
  year={2024}
}

Acknowledgment

Our code is based on the official code of Dspar and MGNN .