Continual learning with high-order experience replay for dynamic network embedding

Published: 01 Jan 2025, Last Modified: 22 May 2025Pattern Recognit. 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Highlights•We develop CLDNE to derive generalized representations for dynamic networks.•We propose a streaming graph auto-encoder in CLDNE to learn global patterns.•We design an experience replay buffer to replay high-order historical topology.•We highlight static historical features using a knowledge distillation module.
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