DATA SCIENCE & SYSTEM SECURITY
PROJECTS
PEOPLE
PUBLICATIONS
PATENTS
In many big data applications, data with complex structures are connected for their explicit/implicit interactions and are naturally represented as graphs/networks. The world is full of complex and dynamic interactions between diverse objects. The flood of dynamic graph data poses great computational challenges and entails interdisciplinary collaborations.
This project aims to develop innovative dynamic graphs analysis (DGA) engines to extract temporal and topological features from complex connected data. DGA offers explainable knowledge learned from dynamic graphs modeling the complex world to support high-quality decision-making in various businesses. GNN, reservoir sampling, contrastive learning, etc, are core technologies to support various real-world DGA applications.
In addition to the new engine development, we are also targeting to build innovative solutions for wide applications where time matters, such as social recommendation, traffic forecasting, medical imaging, financial/banking management, energy, and smart grid optimization, to name a few.
Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media. Existing network embedding based methods have mostly focused on learning good node representations, whereas largely ignoring the subgraph structural changes
