SIGMA Research Group

Data mining, machine learning, and intelligent systems for complex real-world data.

Based at the Institute of Artificial Intelligence, Ocean University of China, we design scalable models and algorithms for urban computing, recommendation, transportation, and spatiotemporal applications.

What we work on

Graph learning

Representation learning and scalable algorithms for complex heterogeneous networks.

Spatiotemporal intelligence

Models that understand movement, time, place, and the dynamics of urban systems.

Applied data mining

Research that connects principled methods with transportation, recommendation, and social applications.

Research directions

Spatiotemporal Data Mining

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

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

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

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.

Latest news

Recent publications, awards, and group updates

13 Jun 2026

One paper was accepted by SIGMOD 2027 (Kunyu Ni as the first author).

17 May 2026

One paper was accepted by the KDD 2026 AI4Science Track (Shuo Lian as the co-first author).

01 May 2026

Four papers were accepted by IJCAI 2026 (Shaoxuan Gu, Liangkun Chen, Jingjing Zhu, and Yingjie Chen as first authors).

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Work with us

Meet the people behind the research

We are a collaborative group working across data mining, machine learning, and intelligent systems. Learn about our members, projects, and recent work.

Meet the team →

LINKS

Institute of Artificial Intelligence Faculty of Information Science and Engineering