Measuring Temporal Lags in Delay-Tolerant Networks
Bibliographic record
Abstract
Delay-tolerant networks (DTNs) are characterized by a possible absence of end-to-end communication routes at any instant. Yet, connectivity can be achieved over time and space, leading to evaluate a given route both in terms of topological length or temporal length. The problem of measuring temporal distances in a social network was recently addressed through postprocessing contact traces like email data sets, in which all contacts are punctual in time (i.e., they have no duration). We focus on the distributed version of this problem and address the more general case that contacts can have arbitrary durations (i.e., be nonpunctual). Precisely, we ask whether each node in a network can track in real time how "out-of-dateâ it is with respect to every other. Although relatively straightforward with punctual contacts, this problem is substantially more complex with arbitrarily long contacts: consecutive hops of an optimal route may either be disconnected (intermittent connectedness of DTNs) or connected (i.e., the presence of links overlaps in time, implying a continuum of path opportunities). The problem is further complicated (and yet, more realistic) by the fact that we address continuous-time systems and nonnegligible message latencies (time to propagate a single message over a single link); however, this latency is assumed fixed and known. We demonstrate the problem is solvable in this general context by generalizing a time-measurement vector clock construct to the case of "nonpunctualâ causality, which results in a tool we call T-Clocks, of independent interest. The remainder of the paper shows how T-Clocks can be leveraged to solve concrete problems such as learning foremost broadcast trees (BTs), network backbones, or fastest broadcast trees in periodic DTNs.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".