Junghyo Jo

Reinforcement Learning for Graph Generation under a Hard Assortativity Constraint

It is shown that a reinforcement learning framework can drive a graph through degree-preserving rewirings to satisfy a prescribed assortativity, which characterizes the degree--degree correlation of adjacent nodes, establishing reinforcement learning as a practical paradigm for hard-constrained graph generation.