For more details, please refer to our paper "Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification"
A meta-learning based framework for few-shot learning on graphs.For more details, please refer to our paper "Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification"
## Environments
- python 3.6
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@@ -10,9 +10,12 @@ For more details, please refer to our paper "Adaptive-Step Graph Meta-Learner fo
- torch-sparse 0.4.3
## Dataset
For origin TRIANGLES dataset, you can download from here. In experiments, we use TRIANGLES with the partition rules of Jatin Chauhan.
In experiments, we use [TRIANGLES](https://drive.google.com/drive/folders/1na8l6DV7qtYIoteFGIp9p7VfQNjmSQxx?usp=sharingwith) with the partition rules of Jatin Chauhan's [paper](https://openreview.net/forum?id=Bkeeca4Kvr). For origin TRIANGLES dataset, you can download it from [here](https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets)
## Training and Test
To train the AS-MAML framework with GraghSAGE and SAGPool, please run:
python main.py
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:
