27 citations · 31 across the 6 of their papers we have counts for
6 papers
Discrete-state Continuous-time Diffusion for Graph Generation
Zhe Xu, Ruizhong Qiu, Yuzhong Chen +6
Graph is a prevalent discrete data structure, whose generation has wide applications such as drug discovery and circuit design. Diffusion generative models, as an emerging research…
Fine-grained Graph Rationalization
Zhe Xu, Menghai Pan, Yuzhong Chen +4
Rationale discovery is defined as finding a subset of the input data that maximally supports the prediction of downstream tasks. In the context of graph machine learning, graph rat…
Calliope-Net: Automatic Generation of Graph Data Facts via Annotated Node-link Diagrams
Qing Chen, Nan Chen, Wei Shuai +4
Graph or network data are widely studied in both data mining and visualization communities to review the relationship among different entities and groups. The data facts derived fr…
Class-Imbalanced Graph Learning without Class Rebalancing
Zhining Liu, Ruizhong Qiu, Zhichen Zeng +7
Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph learning models. Most existing studies are rooted in a class-rebalancing (…
Optimal Propagation for Graph Neural Networks
Beidi Zhao, Boxin Du, Zhe Xu +2
Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input. However, the initial input graph…
Data Augmentation for Deep Graph Learning: A Survey
Kaize Ding, Zhe Xu, Hanghang Tong +1
Graph neural networks, a powerful deep learning tool to model graph-structured data, have demonstrated remarkable performance on numerous graph learning tasks. To address the data…