Abstract: The non-Euclidean nature of graphs made them inaccessible to standard deep learning techniques that rely on fixed-size, ordered inputs. Graph Neural Networks (GNNs) are essential for serving ...
Abstract: Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity and can only handle graphs with at most thousands of nodes. To this ...
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