HCPN-GCN: Scaling Hierarchical Prototype Networks with Cone Geometry for Continual Graph Learning
HCPN-GCN is proposed, a graph-aware extension of HPN that replaces the original linear feature extractors with Graph Convolutional Networks (GCNs) and introduces cone-based prototypes with a diversity regularization objective, which produces richer graph-aware representations while compactly modeling the embedding spac...
S. Silva, V. L. S. Freitas, Gladston J. P. Moreira et al.
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