Graph Neural Networks

Theoretical Foundations of Efficient and Scalable Graph Learning

Dr. Ziang Chen, Instructor, Applied Mathematics, MIT, USA

Feb 24, 12:00 - 13:00

Auditorium between Building 2 - 3, L0 R0215

Graph Neural Networks

This talk addresses key challenges in graph neural networks (GNNs), including expressive power and model depth, by theoretically analyzing and providing solutions in three areas: universal approximation of linear program properties, strong expressive power of subgraph GNNs on graphs with bounded cycles, and the role of residual connections in mitigating oversmoothing and enabling deeper models.