6 citations · 13 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
A Survey on False Information Detection: From A Perspective of Propagation on Social Networks
Kun Xie, Sibo Wang
The proliferation of false information in the digital age has become a pressing concern, necessitating the development of effective and robust detection methods. This paper offers…
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…
Fast and Accurate Antibody Sequence Design via Structure Retrieval
Xingyi Zhang, Kun Xie, Ningqiao Huang +5
Recent advancements in protein design have leveraged diffusion models to generate structural scaffolds, followed by a process known as protein inverse folding, which involves seque…
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…