“Graph Power: Unlocking Real-World Challenges”
Explore our research highlights and PhD opportunities.
We are a research team at the School of Computing, Australian National University. Our overarching research goal is to explore and understand graph-structured data. In today’s real-world applications, graphs are ubiquitously used for representing complex objects and their relationships such as cities in a road network, atoms in a molecule, friendships in social networks, connections in computer networks, and links among web pages. We focus on the following research areas:
- Graph theory and algorithms
- Machine learning on graphs
- Bridging graph theory and machine learning
Explore where we are …
News
1 Feb 2025We have another paper Divide-and-Conquer: Scalable Shortest Path Counting on Large Road Networks accepted to SIGMOD 2025. Congratulations, everyone!
23 Jan 2025We have two papers accepted to ICLR 2025. Congratulations to Gathika, Sean, and all the co-authors!
10 Dec 2024Fangbing's work "Asymmetric Learning for Spectral Graph Neural Networks" is accepted to AAAI 2025. Congratulations, Fangbing!
10 Dec 2024Asela's work "DeepSN: A Sheaf Neural Framework for Influence Maximization" is accepted to AAAI 2025. Congratulations, Asela!
1 Nov 2024Our latest work on dynamic road networks "Dual-Hierarchy Labelling: Scaling Up Distance Queries on Dynamic Road Networks" is accepted by SIGMOD 2025.
22 Oct 2024Our work "Optimal Partial Graph Matching" is available.
16 Oct 2024Our recent work "Towards Bridging Generalization and Expressivity of Graph Neural Networks" is available.
2 Oct 2024Our paper "Stable Tree Labelling for Accelerating Distance Queries on Dynamic Road Networks" is to appear at EDBT 2025.
9 Feb 2024Congratulations to Farhan for receiving a research grant from Helmholtz Information & Data Science Academy!