About: The attention mechanism has demonstrated superior performance for inference over nodes in graph neural networks (GNNs), however, they result in a high computational burden during both training and inference. We propose FastGAT, a method to make attention based GNNs lightweight by using spectral sparsification to generate an optimal pruning of the input graph. This results in a per-epoch time that is almost linear in the number of graph nodes as opposed to quadratic. Further, we provide a re-formulation of a specific attention based GNN, Graph Attention Network (GAT) that interprets it as a graph convolution method using the random walk normalized graph Laplacian. Using this framework, we theoretically prove that spectral sparsification preserves the features computed by the GAT model, thereby justifying our FastGAT algorithm. We experimentally evaluate FastGAT on several large real world graph datasets for node classification tasks, FastGAT can dramatically reduce (up to 10x) the computational time and memory requirements, allowing the usage of attention based GNNs on large graphs.   Goto Sponge  NotDistinct  Permalink

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  • The attention mechanism has demonstrated superior performance for inference over nodes in graph neural networks (GNNs), however, they result in a high computational burden during both training and inference. We propose FastGAT, a method to make attention based GNNs lightweight by using spectral sparsification to generate an optimal pruning of the input graph. This results in a per-epoch time that is almost linear in the number of graph nodes as opposed to quadratic. Further, we provide a re-formulation of a specific attention based GNN, Graph Attention Network (GAT) that interprets it as a graph convolution method using the random walk normalized graph Laplacian. Using this framework, we theoretically prove that spectral sparsification preserves the features computed by the GAT model, thereby justifying our FastGAT algorithm. We experimentally evaluate FastGAT on several large real world graph datasets for node classification tasks, FastGAT can dramatically reduce (up to 10x) the computational time and memory requirements, allowing the usage of attention based GNNs on large graphs.
Subject
  • Graph theory
  • Reasoning
  • Concepts in logic
  • Digital electronics
  • Logic and statistics
  • Logical consequence
  • Sources of knowledge
  • Variants of random walks
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