21 citations · 21 across the 3 of their papers we have counts for
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cs.LG2024★ 21 cited
CLDG: Contrastive Learning on Dynamic Graphs
Yiming Xu, Bin Shi, Teng Ma +3
The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of th…
cs.CV2024
Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning
Rui Zhao, Bin Shi, Jianfei Ruan +2
In noisy label learning, estimating noisy class posteriors plays a fundamental role for developing consistent classifiers, as it forms the basis for estimating clean class posterio…
cs.SI2024
GraphPub: Generation of Differential Privacy Graph with High Availability
Wanghan Xu, Bin Shi, Ao Liu +2
In recent years, with the rapid development of graph neural networks (GNN), more and more graph datasets have been published for GNN tasks. However, when an upstream data owner pub…