6 papers
FracAug: Fractional Augmentation boost Graph-level Anomaly Detection under Limited Supervision
Xiangyu Dong, Xingyi Zhang, Sibo Wang
Graph-level anomaly detection (GAD) is critical in diverse domains such as drug discovery, yet high labeling costs and dataset imbalance hamper the performance of Graph Neural Netw…
Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator
Yusong Zhang, Kun Xie, Xingyi Zhang +2
The spread of rumors on social media, particularly during significant events like the US elections and the COVID-19 pandemic, poses a serious threat to social stability and public…
Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection
Xiping Li, Xiangyu Dong, Xingyi Zhang +7
Graph Anomaly Detection (GAD) in heterogeneous networks presents unique challenges due to node and edge heterogeneity. Existing Graph Neural Network (GNN) methods primarily focus o…
Towards More Accurate Full-Atom Antibody Co-Design
Jiayang Wu, Xingyi Zhang, Xiangyu Dong +5
Antibody co-design represents a critical frontier in drug development, where accurate prediction of both 1D sequence and 3D structure of complementarity-determining regions (CDRs)…
SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection with Extremely Limited Labels
Xiangyu Dong, Xingyi Zhang, Lei Chen +2
Node Anomaly Detection (NAD) has gained significant attention in the deep learning community due to its diverse applications in real-world scenarios. Existing NAD methods primarily…
SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection
Xiangyu Dong, Xingyi Zhang, Yanni Sun +3
The smoothing issue in graph learning leads to indistinguishable node representations, posing significant challenges for graph-related tasks. However, our experiments reveal that t…