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Augmentation-Free Self-Supervised Learning on Graphs
<p align="center"> <a href="https://pytorch.org/" alt="PyTorch"> <img src="https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?e&logo=PyTorch&logoColor=white" /></a> <a href="https://aaai.org/Conferences/AAAI-22/" alt="Conference"> <img src="https://img.shields.io/badge/AAAI'22-brightgreen" /></a> </p>The official source code for Augmentation-Free Self-Supervised Learning on Graphs paper, accepted at AAAI 2022.
Overview
Inspired by the recent success of self-supervised methods applied on images, self-supervised learning on graph structured data has seen rapid growth especially centered on augmentation-based contrastive methods. However, we argue that without carefully designed augmentation techniques, augmentations on graphs may behave arbitrarily in that the underlying semantics of graphs can drastically change. As a consequence, the performance of existing augmentation-based methods is highly dependent on the choice of augmentation scheme, i.e., hyperparameters associated with augmentations. In this paper, we propose a novel augmentation-free self-supervised learning framework for graphs, named AFGRL. Specifically, we generate an alternative view of a graph by discovering nodes that share the local structural information and the global semantics with the graph. Extensive experiments towards various node-level tasks, i.e., node classification, clustering, and similarity search on various real-world datasets demonstrate the superiority of AFGRL.
<img src="img/augmentation.svg" width="700px"></img>
Augmentations on images keep the underlying semantics, whereas augmentations on graphs may unexpectedly change the semantics.
Requirements
- Python version: 3.7.10
- Pytorch version: 1.8.1
- torch-geometric version: 1.7.0
- faiss: 1.7.0
Hyperparameters
Following Options can be passed to main.py
--dataset:
Name of the dataset. Supported names are: wikics, cs, computers, photo, and physics. Default is wikics.
usage example :--dataset wikics
--task:
Name of the task. Supported names are: node, clustering, similarity. Default is node.
usage example :--task node
--layers:
The number of units of each layer of the GNN. Default is [256]
usage example :--layers 256
--pred_hid:
The number of hidden units of predictor. Default is [512]
usage example :--pred_hid 512
--topk:
The number of neighbors for nearest neighborhood search. Default is 4.
usage example :--topk 4
--num_centroids:
The number of centroids for K-means Clustering . Default is 100.
usage example :--num_centroids 100
--num_kmeans:
The number of iterations for K-means Clustering . Default is 5.
usage example :--num_kmeans 5
How to Run
You can run the model with following options
- To run node classification (reproduce Table 2 in paper)
sh run_node_classification.sh
- To run node clustering (reproduce Table 3 in paper)
sh run_node_clustering.sh
- To run similarity search (reproduce Table 4 in paper)
sh run_similarity_search.sh
- or you can run the file with above mentioned hyperparameters
python main.py --embedder AFGRL --dataset wikics --task node --layers [1024] --pred_hid 2048 --lr 0.001 --topk 8
Cite (Bibtex)
- If you find
AFGRL
useful in your research, please cite the following paper:- Lee, Namkyeong, Junseok Lee, and Chanyoung Park. "Augmentation-Free Self-Supervised Learning on Graphs." AAAI 2022.
- Bibtex
@article{lee2021augmentation,
title={Augmentation-Free Self-Supervised Learning on Graphs},
author={Lee, Namkyeong and Lee, Junseok and Park, Chanyoung},
booktitle={AAAI},
year={2022}
}