Benchmarking Graph Neural Networks on Link Prediction

Xing Wang; Alexander Vinel

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arXiv · 2102.12557

Benchmarking Graph Neural Networks on Link Prediction

Xing Wang·Alexander Vinel

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Abstract

In this paper, we benchmark several existing graph neural network (GNN) models on different datasets for link predictions. In particular, the graph convolutional network (GCN), GraphSAGE, graph attention network (GAT) as well as variational graph auto-encoder (VGAE) are implemented dedicated to link prediction tasks, in-depth analysis are performed, and results from several different papers are replicated, also a more fair and systematic comparison are provided. Our experiments show these GNN architectures perform similarly on various benchmarks for link prediction tasks.

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Xing Wang, Alexander Vinel. 2021-02-24. Benchmarking Graph Neural Networks on Link Prediction. https://arxiv.org/abs/2102.12557

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