Edge Stabilizability of Multiagent Systems
Bibliographic record
Abstract
For some natural networks, edge dynamics are a scientific representation, and physical quantities can be better characterized by edges than nodes, such as transportation and social networks. Topology is a paramount determinant for characterizing system performance. To bridge the gaps between the topology structure and stabilizability, we propose a technique to achieve the desired independent strongly connected component (iSCC) partition by adding edges to change the topology structure. Besides, based on iSCC partition as the central tool for grasping the stabilizability of node and edge dynamics, it has been proven that the stabilizability realization of first-order multiagent systems directly depends on the topology structure. Furthermore, the relationship between node stabilizability and edge stabilizability is explored from a graph theory perspective through the transformation mechanism from a node digraph to an edge digraph. In particular, the stabilizability results of second-order multiagent systems reveal that the stabilizability of node dynamics and edge dynamics depends not only on the topology, but also on the feedback coefficients <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub>,<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>. Ultimately, simulation experiments are provided to verify the correctness and effectiveness of the proposed control protocol.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".