The Algorithmic Phase Transition of Random Graph Alignment Problem

We study the graph alignment problem over two independent Erdős-Rényi graphs on $n$ vertices, with edge density $p$ falling into two regimes separated by the critical window around $p_c=\sqrt{\log n\mathrm{/}n}$. Our result reveals an algorithmic phase transition for this random optimization problem: polynomial-time approximation schemes exist in the sparse regime, while statistical-computational gap emerges in the dense regime. Additionally, we establish a sharp transition on the performance of online algorithms for this problem when $p$ lies in the dense regime, resulting in a $\sqrt{8\mathrm{/}9}$ multiplicative constant factor gap between achievable and optimal solutions.