Further , we provide a re - formulation of a specific attention based GNN , Graph Attention Network ( GAT )
that spectral sparsification preserves the features
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 .