21 citations · 36 across the 9 of their papers we have counts for
5 papers · 1 filter
Efficient Memory Crystallization for Graph Learning under Non-Stationary Distribution Shifts
Yue Hou, Ruomei Liu, Yingke Su +2
Deep graph learning models deployed in real-world systems often need to cope with non-stationary environments, where the underlying graph distribution drifts continually over time.…
NC-NCD: Novel Class Discovery for Node Classification
Yue Hou, Xueyuan Chen, He Zhu +5
Novel Class Discovery (NCD) involves identifying new categories within unlabeled data by utilizing knowledge acquired from previously established categories. However, existing NCD…
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
Junran Wu, Xueyuan Chen, Bowen Shi +2
In contrastive learning, the choice of ``view'' controls the information that the representation captures and influences the performance of the model. However, leading graph contra…
Structural Entropy Guided Graph Hierarchical Pooling
Junran Wu, Xueyuan Chen, Ke Xu +1
Following the success of convolution on non-Euclidean space, the corresponding pooling approaches have also been validated on various tasks regarding graphs. However, because of th…
A Simple yet Effective Method for Graph Classification
Junran Wu, Shangzhe Li, Jianhao Li +2
In deep neural networks, better results can often be obtained by increasing the complexity of previously developed basic models. However, it is unclear whether there is a way to bo…