1 citations · 1 across the 2 of their papers we have counts for
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Can LLMs Alleviate Catastrophic Forgetting in Graph Continual Learning? A Systematic Study
Ziyang Cheng, Zhixun Li, Yuhan Li +6
Nowadays, real-world data, including graph-structure data, often arrives in a streaming manner, which means that learning systems need to continuously acquire new knowledge without…
TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting
Yifan Hu, Guibin Zhang, Peiyuan Liu +6
Time series forecasting methods generally fall into two main categories: Channel Independent (CI) and Channel Dependent (CD) strategies. While CI overlooks important covariate rela…
MasRouter: Learning to Route LLMs for Multi-Agent Systems
Yanwei Yue, Guibin Zhang, Boyang Liu +4
Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often incur significant costs and face…
Fast Track to Winning Tickets: Repowering One-Shot Pruning for Graph Neural Networks
Yanwei Yue, Guibin Zhang, Haoran Yang +1
Graph Neural Networks (GNNs) demonstrate superior performance in various graph learning tasks, yet their wider real-world application is hindered by the computational overhead when…
GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
Guibin Zhang, Haonan Dong, Yuchen Zhang +7
Training high-quality deep models necessitates vast amounts of data, resulting in overwhelming computational and memory demands. Recently, data pruning, distillation, and coreset s…