  We consider the problem of a graph subjected to adversarial perturbations,
such as those arising from cyber-attacks, where edges are covertly added or
removed. The adversarial perturbations occur during the transmission of the
graph between a sender and a receiver. To counteract potential perturbations,
we explore a repetition coding scheme with sender-assigned binary noise and
majority voting on the receiver's end to rectify the graph's structure. Our
approach operates without prior knowledge of the attack's characteristics. We
provide an analytical derivation of a bound on the number of repetitions needed
to satisfy probabilistic constraints on the quality of the reconstructed graph.
We show that the method can accurately decode graphs that were subjected to
non-random edge removal, namely, those connected to vertices with the highest
eigenvector centrality, in addition to random addition and removal of edges by
the attacker.
