8 citations · 19 across the 9 of their papers we have counts for
9 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…
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)…
Towards Deeper Understanding of PPR-based Embedding Approaches: A Topological Perspective
Xingyi Zhang, Zixuan Weng, Sibo Wang
Node embedding learns low-dimensional vectors for nodes in the graph. Recent state-of-the-art embedding approaches take Personalized PageRank (PPR) as the proximity measure and fac…
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…
Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection
Xiangyu Dong, Xingyi Zhang, Sibo Wang
Graph-level anomaly detection has gained significant attention as it finds applications in various domains, such as cancer diagnosis and enzyme prediction. However, existing method…
Self-supervised Graph Learning for Long-tailed Cognitive Diagnosis
Shanshan Wang, Zhen Zeng, Xun Yang +1
Cognitive diagnosis is a fundamental yet critical research task in the field of intelligent education, which aims to discover the proficiency level of different students on specifi…
Learning Based Proximity Matrix Factorization for Node Embedding
Xingyi Zhang, Kun Xie, Sibo Wang +1
Node embedding learns a low-dimensional representation for each node in the graph. Recent progress on node embedding shows that proximity matrix factorization methods gain superb p…