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HERO: Preserving Structure and Semantics in Heterogeneous Continual Graph Learning
Machine learning on heterogeneous graphs has advanced rapidly, driven by the diverse entities, relations, and semantics inherent in real-world data. However, most existing studies assume static graphs, whereas real-world graph data are often continuously updated. Continual learning in this setting i
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