Sharpening the Universality of Network Resilience Patterns using Motifs
Bibliographic record
Abstract
Abstract The resilience of networked systems to perturbations is a fundamental problem with applications to ecosystem management, financial system stability, and cell reprogramming. This key challenge is that in high-dimensional systems, there is no “oracle” that can predict, a priori, which changes to a nonlinear system’s parameters will be harmless vs. which will cause a system-wide failure (bifurcation). Here, we present a proof of principle using the Florida Bay food web network, showing how one can use higher-order network structure to arrive at a reliable, universal scalar indicator of a system’s proximity to a bifurcation. Our framework builds on and sharpens a recently introduced mean-field theory for nonlinear dynamics on networks. We find that by incorporating information on high-order network structure in the form of network motifs, the prediction of resilience is greatly improved, especially near a bifurcation point. Our results stress the key role of higher-order structure in driving a system’s dynamics, offering new ways to anticipate and prevent the collapse of large networks raging from ecosystems to infrastructure networks.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".