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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".