Global Robustness and Identifiability of Random, Scale‐Free, and Small‐World Networks
Bibliographic record
Abstract
We are interested in the relationships among network topology, robustness, and identifiability, and their implications in improving network reconstruction. We used three different types of artificial gene networks (AGNs) with distinct topologies: topologies random (RND), scale-free (SF), and small-world (SW), to investigate their robustness and identifiability. The robustness of a network is represented by structural reachability (existence of pathways between two nodes) and dynamic reachability (response on one node upon perturbation on another node). The identifiability of the network edges is assessed in silico with an established reverse-engineering algorithm. We found that (1) structural reachability does not always lead to dynamic reachability; (2) network robustness is high and identifiability is low in all surveyed AGNs; (3) robustness is more sensitive to network topologies than is identifiability. We also devised a method for network dissection in which three subnets (set of alternative pathways or feedbacks, referred to as pathnet) are related to each node pair. This method allows us to identify the fine structural features underlying the distinct behaviors of the networks. For example, pathnet of the edge tail negatively contributes to the edge identifiability, and it is likely that extra perturbation at this pathnet would improve edge identifiability. We provide a case study to prove that double perturbations decrease the edge robustness and increase structural identifiability with a T helper cell-differentiation network model.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".