Reconsidering the Performance of GAE in Link Prediction

• URL: http://arxiv.org/abs/2411.03845v1
• Date: Wed, 06 Nov 2024 11:29:47 GMT
• Title: Reconsidering the Performance of GAE in Link Prediction
• Authors: Weishuo Ma, Yanbo Wang, Xiyuan Wang, Muhan Zhang,
• Abstract summary: We investigate the potential of Graph Autoencoders (GAE) Our findings reveal that a well-optimized GAE can match the performance of more complex models while offering greater computational efficiency.
• Score: 27.038895601935195
• License:
• 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 However, outdated baseline models may lead to an overestimation of the benefits provided by these novel approaches.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.

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