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Reconstructing Undirected Graphs from Eigenspaces

Yohann De Castro, Thibault Espinasse, Paul Rochet; 18(51):1−24, 2017.


We aim at recovering the weighted adjacency matrix $\mathsf{W}$ of an undirected graph from a perturbed version of its eigenspaces. This situation arises for instance when working with stationary signals on graphs or Markov chains observed at random times. Our approach relies on minimizing a cost function based on the Frobenius norm of the commutator $\mathsf{A} \mathsf{B}-\mathsf{B} \mathsf{A}$ between symmetric matrices $\mathsf{A}$ and $\mathsf{B}$. We describe a particular framework in which we have access to an estimation of the eigenspaces and provide support selection procedures from theoretical and practical points of view. In the Erdős-Rényi model on $N$ vertices with no self-loops, we show that identifiability (i.e., the ability to reconstruct $\mathsf{W}$ from the knowledge of its eigenspaces) follows a sharp phase transition on the expected number of edges with threshold function $N\log N/2$. Simulated and real life numerical experiments assert our methodology.

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