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Papers/Reconsidering the Performance of GAE in Link Prediction

Reconsidering the Performance of GAE in Link Prediction

Weishuo Ma, Yanbo Wang, Xiyuan Wang, Muhan Zhang

2024-11-06PredictionLink Prediction
PaperPDFCode(official)Code

Abstract

Various graph neural networks (GNNs) with advanced training techniques and model designs have been proposed for link prediction tasks. However, outdated baseline models may lead to an overestimation of the benefits provided by these novel approaches. To address this, we systematically investigate the potential of Graph Autoencoders (GAE) by meticulously tuning hyperparameters and utilizing the trick of orthogonal embedding and linear propagation. Our findings reveal that a well-optimized GAE can match the performance of more complex models while offering greater computational efficiency.

Results

TaskDatasetMetricValueModel
Link Property Predictionogbl-ddiNumber of params13816833Refined-GAE
Link Property Predictionogbl-collabNumber of params126669825Refined-GAE
Link Property Predictionogbl-ppaNumber of params295848449Refined-GAE

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