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<p> <img src="imgs/s2gae_figure_wsdm.png" width="1000"> <br /> </p> <h1> S2GAE: Self-Supervised Graph Autoencoder </h1>

This is the PyG implementation for WSDM'23 paper: S2GAE: Self-Supervised Graph Autoencoders Are Generalizable Learners with Graph Masking

S2GAE is a generalized self-supervised graph representation learning method, which achieves competitive or better performance than existing state-of-the-art methods on different types of tasks including node classification, link prediction, graph classification, and molecular property prediction.

<h2>Installation</h2>

The required packages can be installed by running pip install -r requirements.txt.

<h2>Datasets</h2> The datasets used in our paper can be automatically downlowad. <h2>Quick Start</h2> For quick start, you could try:

Node classification (Cora, CiteSeer, and PubMed)

python s2gae_nc_acc.py --dataset `Cora`

Link prediction (ogbl-ddi, ogbl-collab, and ogbl-ppa)

python s2gae_large_lp.py --dataset "ogbl-ddi" 

Graph classification

<h2> Experimental Results</h2>

Node classification (Accuracy, %)

CoraCiteSeerPubMedA-ComputeA-PhotoCoauthor-CSCoauthor-PhysicsOgbn-arxivOgbn-proteins
MVGRL85.86±0.1573.18±0.2284.86±0.3188.70±0.2492.15±0.2092.87±0.1395.35±0.0868.33±0.31-
BGRL86.16±0.2073.96±0.1486.42±0.1890.48±0.1093.22±0.1593.35±0.0696.16±0.0971.77±0.19_
GraphMAE85.45±0.4072.48±0.7785.74±0.1488.04±0.6192.73±0.1793.47±0.0496.13±0.0371.86±0.0060.99±0.21
MaskGAE87.31±0.0575.20±0.0786.56±0.2690.52±0.0493.33±0.1492.31±0.0595.79±0.0270.99±0.1261.23±0.19
S2GAE(ours)86.15±0.2574.60±0.0686.91±0.2890.94±0.0893.61±0.1091.70±0.0895.82±0.0372.02±0.0563.33±0.12

Link prediction (AUC)

CoraCiteSeerPubMedBlogcatalogFlickrOgbl-ddiOgbl-collabOgbl-ppa
AUCAUCAUCAUCAUCAUCHits@20Hits@50Hits@10
GAE91.09±0.0190.52±0.0496.40±0.0184.91±1.4492.50±0.4037.07±5.0744.75±1.072.52±0.47
GraphMAE89.19±0.0091.20±0.1193.72±0.0076.60±1.3288.69±0.04-22.79±1.620.18±0.28
MaskGAE96.66±0.1798.00±0.2399.06±0.0581.06±3.0693.60±0.1416.25±1.6032.47±0.590.23±0.04
S2GAE(ours)95.05±0.7694.85±0.4997.38±0.1787.06±0.3794.38±0.0265.91±3.5054.74±1.063.98±1.33

Graph classification (Accuracy, %)

IMDB-BIMDB-MPROTEINSCOLLABMUTAGREDDIT-BNCI1
InfoGraph73.03±0.8749.69±0.5374.44±0.3170.65±1.1391.20±1.30-76.20±1.06
GraphCL71.14±0.4448.58±0.6774.39±0.4571.36±1.1586.80±1.3489.53±0.8477.87±0.41
MVGRL74.20±0.7051.20±0.50--89.70±1.1084.50±0.60-
GraphMAE75.52±0.6651.63±0.5275.30±0.3980.32±0.4688.19±1.2688.01±0.1980.40±0.30
S2GAE(ours)75.76±0.6251.79±0.3676.37±0.4381.02±0.5388.26±0.7687.83±0.2780.80±0.24
<h1>Citing</h1>

If you find this work is helpful to your research, please consider citing our paper:

@inproceedings{tan2023s2gae,
  title={S2GAE: Self-Supervised Graph Autoencoders Are Generalizable Learners with Graph Masking},
  author={Tan, Qiaoyu and Liu, Ninghao and Huang, Xiao and Choi, Soo-Hyun and Li, Li and Chen, Rui and Hu, Xia},
  booktitle={Proceedings of the 16th ACM International Conference on Web Search and Data Mining},
  year={2023}
}
@article{tan2022mgae,
  title={Mgae: Masked autoencoders for self-supervised learning on graphs},
  author={Tan, Qiaoyu and Liu, Ninghao and Huang, Xiao and Chen, Rui and Choi, Soo-Hyun and Hu, Xia},
  journal={arXiv preprint arXiv:2201.02534},
  year={2022}
}