Researches | SIGMA | 中国海洋大学数据挖掘与学习兴趣小组

Spatiotemporal Data Mining

Wed, 31 Jul 2024 00:00:00 +0000

Spatiotemporal Data Mining focuses on extracting knowledge and patterns from massive datasets with temporal and spatial dimensions, particularly large-scale trajectory data from moving objects. It involves developing machine learning-based modeling frameworks and computational paradigms to tackle real-world challenges in downstream spatiotemporal applications.

Graph Neural Networks

Sun, 28 Jul 2024 00:00:00 +0000

Graph Neural Networks (GNNs) specialize in learning effective representation learning for large-scale complex graph-structured data, with particular emphasis on massive multi-layer heterogeneous graph networks. This research domain focuses on developing scalable GNN architectures tailored for complex graph networks with billions of nodes/edges, while enhancing performance across diverse graph mining tasks such as recommendation systems, anomaly detection, and beyond.

Graph Data Mining

Mon, 15 Jul 2024 00:00:00 +0000

Graph Data Mining focuses on discovering frequent and meaningful subgraph patterns (e.g., network motifs) from large-scale temporal graphs. It involves designing highly scalable and parallelizable algorithms to enable efficient mining and analysis of massive temporal graphs on modern multi-core computing architectures.

Intelligent Transportation Systems

Wed, 29 May 2024 00:00:00 +0000

Intelligent Transportation Systems aim to enhance urban mobility by leveraging large-scale traffic observation data and advanced machine learning techniques. This research direction focuses on developing data-driven predictive models for accurate traffic flow estimation, including vehicle and pedestrian dynamics, to optimize existing urban transportation infrastructure.