Graph ML Research from Our Group

Our research group has been working extensively in graph machine learning (ML), particularly in graph neural networks (GNNs). Some of the GNN research is highlighted below. Besides graph ML, we are also working in the broad area of AI. See more details in this link.

1. Graph self-supervised learning (Graph SSL)

2. GNNs (Graph SSL) at Scale

3. GNNs for Time Series Analysis

4. Dynamic Graph Representations (Dynamic GNNs)

5. Knowledge Graph Embedding and Reasoning

6. GNNs for heterophilic graphs

7. GNNs for clustering/community detection

8. GNNS for anomaly detection

9. Defence and attacks in GNNs

10. Privacy in GNNs

11. Graph Few-shot Learning

12. Graph Structure Learning

14. Graph Domain Adaptation

15. Graph Out-of-Distribution Generalization and Detection

16. Graph Similarity Learning

17. GNNs for Recommender Systems

18. GNNs for Drug Discovery

19. Data-centric GNNs

20. PLM meets Graphs

Please contact me if you are interested in any of these topics for further discussions.

Shirui Pan
Shirui Pan
Professor | ARC Future Fellow

My research interests include data mining, machine learning, and graph analysis.