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Class-incremental continual graph learning with adversarial graph condensation
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AI
DOI：10.1016/j.neucom.2026.133420.png)
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Abstract
En 中文
Continual Graph Learning (CGL) enables models to incrementally learn from streaming graph-structured data without forgetting previously acquired knowledge. Experience replay is a common solution that reuses a subset of past samples during training. However, it may lead to information loss and privacy risks. Generative replay addresses these concerns by synthesizing informative subgraphs for rehearsal. Existing generative replay approaches often rely on graph condensation via distribution matching, which faces two key challenges: (1) the use of random feature encodings may fail to capture the characteristic kernel of the discrepancy metric, weakening distribution alignment; and (2) matching over a fixed small subgraph cannot guarantee low risk on previous tasks, as indicated by domain adaptation theory.
Keywords:
Continual Graph Learning
Generative Replay
Graph Condensation
Adversarial Learning
Class-incremental Learning
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