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A meta-learning framework for few-shot graph learning

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AS-MAML

A meta-learning based framework for few-shot learning on graph classification. For more details, please refer to our paper "Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification" that was published on CIKM2020.

Environments

  • python 3.6
  • pytorch 1.3.0
  • torch-cluster 1.4.5
  • torch-geometric 1.3.2
  • torch-scatter 1.4.0
  • torch-sparse 0.4.3

Dataset

In experiments, we use TRIANGLES (click to download) with the partition rules of Jatin Chauhan's paper. Extract the downloaded file and put the files in ./data/TRIANGLES. For Graph-R52 and COIL-DEL can also be downloaded now.

If you need origin dataset of TRIANGLES, you can download it from here

Training and Test

To train the AS-MAML framework with GraghSAGE and SAGPool on TRIANGLES dataset, please run:

python main.py

To test the trained model, please run the following code with specified model path:

python test.py --model_dir *

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