Benchmarking Graph Neural Networks on Link Prediction

• URL: http://arxiv.org/abs/2102.12557v1
• Date: Wed, 24 Feb 2021 20:57:16 GMT
• Title: Benchmarking Graph Neural Networks on Link Prediction
• Authors: Xing Wang, Alexander Vinel
• Abstract summary: We benchmark several existing graph neural network (GNN) models on different datasets for link predictions. Our experiments show these GNN architectures perform similarly on various benchmarks for link prediction tasks.
• Score: 80.2049358846658
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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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