Abstract: The Graph Isomorphism (GI) problem has been extensively studied due to its significant applications. The most effective class of GI algorithms, i.e., canonical labeling algorithms, are ...
Abstract: The graph neural networks (GNNs) have drawn much attention for predicting the remaining useful life (RUL) due to their excellent performance in processing correlation relationship among data ...
We introduce DRESS, a deterministic, parameter-free framework that iteratively refines the structural similarity of edges in a graph to produce a canonical fingerprint: a real-valued edge vector, ...
The plan of the milestones agreed with ARIA. See attached documents Links to the repositories that might be relevant for the project: https://github.com/statusfailed ...
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