Live Exploration of Dynamic Rings
Bibliographic record
Abstract
Almost all the vast literature on graph explorationassumes that the graph is static: its topology does not changeduring the exploration, except for occasional faults. To date, very little is known on exploration of dynamic graphs, wherethe topology is continously changing. The few studies havebeen limited to the centralized (or post-mortem) case, assumingcomplete a priori knowledge of the changes and the times of theiroccurrence, and have only considered fully synchronous systems. In this paper, we start the study of the decentralized (or live) exploration of dynamic graphs, i.e. when the agents operate inthe graph unaware of the location and timing of the changes. Weconsider dynamic rings under the standard 1-interval-connectedrestriction, and investigate the feasibility of their exploration, inboth the fully synchronous and semi-synchronous cases. Whenexploration is possible we examine at what cost, focusing on theminimum number of agents capable of exploring the ring. Weestablish several results highlighting the impact that anonymityand structural knowledge have on the feasibility and complexityof the problem.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".