Abstract: While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do ...
Abstract: Representation learning on continuous-time dynamic graphs (CTDGs) is critical for modeling evolving network behaviors. However, existing methods often fail to capture both temporal dynamics ...
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