Toward scalable cut vertex and link detection with applications in wireless ad hoc networks
Bibliographic record
Abstract
Ad hoc networks are expected to have some critical connectivity properties before partitioning. Timely partition prediction signals action for improving fault tolerance and performing some data or service replication so that the network can continue functioning after partition does occur. This article surveys existing prediction concepts and discusses their scalability, simplicity, correctness, speed, communication overhead, and applications. Existing centralized algorithms declare an edge or a node as critical if its removal will separate the network into several components. Several localized definitions of critical (or cut) nodes and links, and removable nodes, are demonstrated to be simple, useful, and scalable. A node is critical if the subgraph of p-hop neighbors of node (without the node itself) is disconnected. A link is critical if its endpoints have no common p-hop neighbors (assuming that the link between them does not exist). Definitions are extended toward local k-connectivity. The false positives mostly occur when alternative routes exist but are relatively long, and therefore may not provide satisfactory service in applications. Therefore, localized protocols provide faster and often more reliable partition warnings for possible timely replication decisions. This conceptual advance provides ingredients for establishing and restoring biconnectivity.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".