HERO: Preserving Structure and Semantics in Heterogeneous Continual Graph Learning
Heterogeneous continual graph learning framework HERO is introduced, a heterogeneous continual graph learning framework that preserves historical knowledge at both structural and semantic levels while adapting to incoming tasks and aligns task-conditioned predictions and heterogeneous semantic responses through multi-l...
Guiquan Sun, Xikun Zhang, Jingchao Ni et al.
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