7 citations · 7 across the 3 of their papers we have counts for
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cs.LG2024
E-CGL: An Efficient Continual Graph Learner
Jianhao Guo, Zixuan Ni, Yun Zhu +1
Continual learning has emerged as a crucial paradigm for learning from sequential data while preserving previous knowledge. In the realm of continual graph learning, where graphs c…
cs.LG2023
SGL-PT: A Strong Graph Learner with Graph Prompt Tuning
Yun Zhu, Jianhao Guo, Siliang Tang
Recently, much exertion has been paid to design graph self-supervised methods to obtain generalized pre-trained models, and adapt pre-trained models onto downstream tasks through f…
cs.LG2022★ 7 cited
RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive Learning
Yun Zhu, Jianhao Guo, Fei Wu +1
Graph contrastive learning has gained significant progress recently. However, existing works have rarely explored non-aligned node-node contrasting. In this paper, we propose a nov…