Graph isomorphism is a fundamental concept for exploiting 
the structure of graphs. This project aims to develop new 
heuristic techniques and theories for graph isomorphism, 
advancing state-of-the-art methodologies for its 
applications in solving real-world problems. Inspired by 
recent advances in machine learning, this project will 
investigate graph isomorphism problems from a deep 
learning perspective by marrying the best approaches 
from classic graph isomorphism studies with new techniques 
from modern AI. This is an important step towards bridging 
the gap between combinatorical generalization and 
deep learning.