NSKSD: Interdependent Network Dismantling via Nonlinear-Metric
Bibliographic record
Abstract
Most networks are not isolated but interdependent in real applications. Studies on the dismantling of interdependent networks have motivated crucial and significant improvements to our understanding of fundamental network behaviors, i.e., function, robustness, structure characters, etc. The popular way to deal with such a dismantling problem is to extract the influential spreaders based on centrality measures. This brief proposes the node significance (NS) for individual node measures based on the novel sigmoid-like similarity calculation. A new index KSD is proposed to identify the top influential nodes, combining the different node centralities’ effects. Notably, we put forward two types of significance, i.e., the overlapping node significance (ONS) and the overlapping KSD (OKSD); both are set as benchmarks for computing the spreading capability of each node. Simulation results validate that our proposed NSKSD is much better than the state-of-the-art methods in terms of the efficient and effective dismantling of interdependent 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".