35 citations · 43 across the 6 of their papers we have counts for
6 papers · 1 filter
Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-Level Anomaly Detection
Chunjing Xiao, Shikang Pang, Wenxin Tai +3
Graph-level anomaly detection is significant in diverse domains. To improve detection performance, counterfactual graphs have been exploited to benefit the generalization capacity…
Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly Detection
Chunjing Xiao, Shikang Pang, Xovee Xu +3
A critical aspect of Graph Neural Networks (GNNs) is to enhance the node representations by aggregating node neighborhood information. However, when detecting anomalies, the repres…
Predicting Human Mobility via Self-supervised Disentanglement Learning
Qiang Gao, Jinyu Hong, Xovee Xu +3
Deep neural networks have recently achieved considerable improvements in learning human behavioral patterns and individual preferences from massive spatial-temporal trajectories da…
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay
Fan Zhou, Chengtai Cao
Graph Neural Networks (GNNs) have recently received significant research attention due to their superior performance on a variety of graph-related learning tasks. Most of the curre…
Frosting Weights for Better Continual Training
Xiaofeng Zhu, Feng Liu, Goce Trajcevski +1
Training a neural network model can be a lifelong learning process and is a computationally intensive one. A severe adverse effect that may occur in deep neural network models is t…
Meta-GNN: On Few-shot Node Classification in Graph Meta-learning
Fan Zhou, Chengtai Cao, Kunpeng Zhang +3
Meta-learning has received a tremendous recent attention as a possible approach for mimicking human intelligence, i.e., acquiring new knowledge and skills with little or even no de…