Title: The Graph Learning Attention Mechanism: Learnable Sparsification Without Heuristics. Abstract: Graph Neural Networks (GNNs) are local aggregators that derive their expressive power from their sensitivity to network structure. However, this sensitivity comes at a cost: noisy edges degrade performance. In response, many GNNs include edge-weighting mechanisms that scale the contribution of each edge in the aggregation step. However, to account for neighborhoods of varying size, node-embedding mechanisms must normalize these edge-weights across each neighborhood. As such, the impact of noisy edges cannot be eliminated without removing those edges altogether. Motivated by this issue, we introduce the Graph Learning Attention Mechanism (GLAM): a drop-in, differentiable structure learning layer for GNNs that separates the distinct tasks of structure learning and node embedding. In contrast to existing graph learning approaches, GLAM does not require the addition of exogenous structural regularizers or edge-selection heuristics to learn optimal graph structures. In experiments on citation and co-purchase datasets, we demonstrate that our approach can match state of the art semi-supervised node classification accuracies while inducing an order of magnitude greater sparsity than existing graph learning methods.