arXiv Machine Learning July 14, 2026 By Tingxu Yan Ye Yuan

CA-DGCL: Dynamic Graph Continual Learning via Condensation and Attachment

Read the original on arXiv Machine Learning →

arXiv:2607. 11112v1 Announce Type: new Abstract: Dynamic graph continual learning (DGCL) is an effective manner for handling catastrophic forgetting in dynamic graphs.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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UNIT: Unleash Large Language Models Potential for Graph Continual Learning

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FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning

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DYNA : Dynamic Episodic Memory Networks for Augmenting Large Language Models with Temporal Knowledge Graphs in Continuous Learning

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SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport

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G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs

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AL-GNN: Privacy-Preserving and Replay-Free Continual Graph Learning via Analytic Learning

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By Xuling Zhang, Jindong Li, Yifei Zhang, Mingqi Yang, Menglin Yang

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UNIT: Unleash Large Language Models Potential for Graph Continual Learning

FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning

DYNA : Dynamic Episodic Memory Networks for Augmenting Large Language Models with Temporal Knowledge Graphs in Continuous Learning

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