Simplifying Graph Attention Networks with Source-Target Separation

We present a novel Graph Neural Networks (GNN) architecture as an simplification of Graph Attentional Network (GAT) model with implicit computation of edge attention coefficients and shared sparse-dense matrix multiplication between heads. These improvements reduce training time and memory consumption while keeping the model capacity of GAT. On several established benchmarks, our model has a performance on par with state-of-the-art, yet with improved efficiency and scalability similar to simpler models including Graph Convolutional Network (GCN). Notably, we are able to apply the model to the large-scale Reddit social network dataset within a reasonable training time and memory constraint, which is previously infeasible for models with similar complexity including GAT.

Authors

• Hantao Guo
• Rui Yan 0001
• Yansong Feng 0002
• Xuesong Gao
• Zhanxing Zhu

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