grain's Introduction

Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization.

This repository is the official implementation of GRAIN.

Requirements

To install requirements:

pip install -r requirements.txt

Training

To train the model(s) in the paper:

cd the “example” data

run the python file GRAIN(ball-D).py or GRAIN(NN-D).py

Results

Accuracy comparison:

Active learning comparison:

Core-set selection comparison:

Efficiency comparison on GPU:

Efficiency comparison on CPU:

Interpretability:

Ablation study:

Generalization:

Cite

If you use Grain in a scientific publication, we would appreciate citations to the following paper:

@article{zhang2021grain,
  title={GRAIN: improving data efficiency of gra ph neural networks via diversified in fluence maximization},
  author={Zhang, Wentao and Yang, Zhi and Wang, Yexin and Shen, Yu and Li, Yang and Wang, Liang and Cui, Bin},
  journal={Proceedings of the VLDB Endowment},
  volume={14},
  number={11},
  pages={2473--2482},
  year={2021},
  publisher={VLDB Endowment}
}

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grain's Issues

cannot scale to the large-scale dataset Reddit

According to the example code, it needs to compute the distance between arbitrary node pairs, which is O(n^2) complexity and leads to OOM. How can it scale to the large-scale dataset on Reddit?

Thank you~

Cannot reproduce the test accuracy of GRAIN (ball-D)

Hi, I tried to run your code in ./examples/Test.ipynb. But I cannot reproduce the test accuracy of GRAIN (ball-D). The performance is about ~3% lower than that is shown in your paper. Could you check whether the parameters are correct in your file? I attached the figure showing my results.

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