Area of research
Artificial Intelligence · Statistical and Nonlinear Physics
Research interest
Research interests include Computer science, Artificial intelligence, Graph, Anomaly detection, Machine learning, and Time series.
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering
Multimodal Large Language Models for Phishing Webpage Detection and Identification
Efficient Heterogeneous Graph Learning via Random Projection
Deep Long-Tailed Learning: A Survey
Sketch-Based Anomaly Detection in Streaming Graphs
Time Series Anomaly Detection With Adversarial Reconstruction Networks
Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation
When do contrastive learning signals help spatio-temporal graph forecasting?
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
Mixup for Node and Graph Classification
MStream: Fast Anomaly Detection in Multi-Aspect Streams
Spherical Confidence Learning for Face Recognition
Understanding and Resolving Performance Degradation in Deep Graph Convolutional Networks
CurGraph: Curriculum Learning for Graph Classification
Dynamic Graph-Based Anomaly Detection in the Electrical Grid
NodeAug: Semi-Supervised Node Classification with Data Augmentation
BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach