Graphs are everywhere - but how can we reveal meaningful structure in graphs? Nowadays, in many real-world applications, the underlying data is modelled as graphs to represent complex objects and their relationships: cities in a road network, atoms in a molecule, friendships in social networks, connections in computer networks, and links among web pages. However, graphs in real-world applications are often highly complex, dynamic, and very large, which poses great challenges for analysing graphs. Traditional algorithms on graphs primarily aim to explore efficient algorithmic solutions to graph-related problems. In recent years, deep learning techniques such as graph neural networks have emerged to study graph-related problems from a machine learning perspective. In this talk, I will present some of our recent research work in these areas and discuss possible future directions to bridge traditional algorithms on graphs with graph learning.
Meeting ID: 876 4070 3991
Qing joined the School of Computing at ANU in 2012. Prior to that, she was an Information Systems Analyst at Deputy Vice Chancellor's Office (Research), University of Otago, New Zealand. She received her PhD in Computer Science from Christian-Albrechts-University Kiel, Germany, in 2010, and has more than a decade of industry experience in China and New Zealand in the areas of databases, data management and analysis. More information is available at https://cecs.anu.edu.au/people/qing-wang.