Sharp threshold for network recovery from voter model dynamics

Abstract: We investigate the problem of recovering a latent directed Erd\H{o}s-R\'enyi graph $G^*\sim \mathcal G(n,p)$ from observations of discrete voter model trajectories on $G^*$, where $np$ grows polynomially in $n$. Given access to $M$ independent voter model trajectories evolving up to time $T$, we establish that $G^*$ can be recovered \emph{exactly} with probability at least $0.9$ by an \emph{efficient} algorithm, provided that [ M \cdot \min{T, n} \geq C n^2 p^2 \log n ] holds for a sufficiently large constant $C$. Here, $M\cdot \min{T,n}$ can be interpreted as the approximate number of effective update rounds being observed, since the voter model on $G^*$ typically reaches consensus after $\Theta(n)$ rounds, and no further information can be gained after this point. Furthermore, we prove an \emph{information-theoretic} lower bound showing that the above condition is tight up to a constant factor. Our results indicate that the recovery problem does not exhibit a statistical-computational gap.