Construction of the Sparsest Maximally r-Robust Graphs
Haejoon Lee, Dimitra Panagou
TL;DR
This work addresses resilient consensus under misbehaving agents by focusing on $r$-robustness and seeks the sparsest graph topologies that still achieve maximum robustness. It derives tight edge-count lower bounds for $r$-robust graphs at the minimal node counts $2r-1$ and $2r$, and then constructs two classes of graphs—$(2r-1,r)$-robust and $(2r,r)$-robust—that attain these bounds. Theoretical proofs rely on clique-based structures and $r$-reachability arguments, while simulations validate both resilience under the W-MSR algorithm and the sparsity of the proposed constructions. The results provide concrete design guidelines for robust, communication-efficient networked robotics systems and suggest directions for extending the framework to larger graphs and control synthesis to maintain robustness with reduced communication overhead.
Abstract
In recent years, the notion of r-robustness for the communication graph of the network has been introduced to address the challenge of achieving consensus in the presence of misbehaving agents. Higher r-robustness typically implies higher tolerance to malicious information towards achieving resilient consensus, but it also implies more edges for the communication graph. This in turn conflicts with the need to minimize communication due to limited resources in real-world applications (e.g., multi-robot networks). In this paper, our contributions are twofold. (a) We provide the necessary subgraph structures and tight lower bounds on the number of edges required for graphs with a given number of nodes to achieve maximum robustness. (b) We then use the results of (a) to introduce two classes of graphs that maintain maximum robustness with the least number of edges. Our work is validated through a series of simulations.
