Bibliographic record
Abstract
Real networks are often subjected to failures and attacks. Real networks are found to be robust to errors and vulnerable to attacks. This behavior of real networks is attributed to their non-homogeneous degree distribution. Non-homogenous networks are also known as scale free networks. Real networks along with the scale free property show high modularity.;Many network models have been proposed to model real networks. Erdos-Renyi Random network model is the first attempt, but fails to incorporate both properties - scale free as well as the high modularity of the real networks. Small world model shows high clustering but lacks non-homogeneous distribution. The scale free network model has non-homogeneous degree distribution but lacks the modularity. In 2002, Ravasz and Barabasi proposed a Hierarchical Network model that combines non-homogeneous degree distribution as well as high modularity showed by real networks.;The objective of this research is to study the error and attack tolerance of different network models. The static as well as dynamic tolerances of attacks are analyzed. The effect of an attack on the network model is quantified by considering the dynamic flows of quantities and using the impact factors. The results of the study show that though the scale free as well as the hierarchical models are vulnerable to attacks, the performance can be highly secured by protecting some key nodes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".