UNIT: Unleash Large Language Models Potential for Graph Continual Learning
In real-world multimodal web scenarios, graph-structured data often arrives in a streaming manner, making graph continual learning a crucial paradigm for continuously modeling such evolving structures. However, existing graph continual learning methods still face two fundamental challenges. 1) semantic-structural separation, where the graph-based methods excel at modeling topological relationships but neglect deep semantics. 2) imbalanced knowledge transfer, where existing models fail to effectively leverage general knowledge gained from early tasks to benefit subsequent new tasks. To address above issues, we propose a novel framework, \textbf{UN}leash Large Language Models PotentIal for ...
| Authors | Tairan Huang, Yili Wang, Beibei Hu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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