A Graph-Theoretic Equilibrium Analysis of Attacker-Defender Game on Consensus Dynamics Under $\mathcal {H}_2$ Performance Metric
Bibliographic record
Abstract
In this paper, we propose a game-theoretic framework for improving the resilience of the consensus algorithm, under the$\mathcal {H}_2$performance metric, in the presence of a strategic attacker. In this game, an attacker selects a subset of nodes in the network to inject attack signals. Its objective is to maximize the$\mathcal {H}_2$norm of the system from the attack signal to the output of the system. The defender improves the resilience of the system by adding self-feedback loops to certain nodes of the network to minimize the system's norm. We investigate the interplay between the equilibrium strategies of the game and the underlying connectivity graph, using the$\mathcal {H}_2$performance metric as the game pay-off. The equilibrium of the (zero-sum) attacker-defender game determines the optimal location of the defense nodes in the network. The existence of a Nash equilibrium for consensus dynamics is studied under undirected and directed network topologies. For the cases where the attacker-defender game does not admit a Nash equilibrium, the Stackelberg equilibrium of the game is studied with the defender as the game leader. Our results indicate that the equilibrium strategies of the game are characterized by graph-theoretic notions such as network centrality metrics. In particular, we show that theeffective centerof the graph, a new network centrality measure, captures the optimal location of defense nodes in undirected networks. In directed networks, however, the optimal locations of defenders are those nodes with small in-degrees. The theoretical results are applied to the design of a resilient formation of vehicle platoons.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".