Reachability-Based Robustness of Network Controllability under Node and Edge Attacks
Bibliographic record
Abstract
In real world applications, control is always performed without perfect knowledge, perfect models, and often, under changing conditions. Such circumstances are particularly true of complex systems. As a result, application of control theory to complex systems requires the development and implementation of control policies that are robust to unexpected and potentially malicious changes to the underlying network. This paper makes three important contributions along this direction. First, we introduce a new definition of robustness which captures realistic constraints imposed by many control problems. Second, we develop a novel algorithm for computing this robustness measure. Third, we conduct a thorough assessment of the control robustness of different synthetic networks to a wide array of attacks/network perturbations. We find that our robustness measure is behaviorally different from other robustness measurements in the literature and that the attacks considered highlight a number of ways in which network properties correlate with control robustness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".