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…
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…
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…
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…
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…