A Distributed Algorithm for Min-Max Tree and Max-Min Cut Problems in Communication Networks
Bibliographic record
Abstract
We consider the problem of finding a multicast tree rooted at the source node and including all the destination nodes such that the maximum weight of the tree arcs is minimized. It is of paramount importance for many optimization problems,e.g.,the maximum-lifetime multicast problem in multihop wireless networks, in the data networking community. We explore some important properties of this problem from a graph theory perspective and obtain a min-max-tree max-min-cut theorem, which provides a unified explanation for some important while separated results in the recent literature. We also apply the theorem to derive an algorithm that can construct a global optimal min-max multicast tree in a distributed fashion. In random networks withnnodes andmarcs, our theoretical analysis shows that the expected communication complexity of our distributed algorithm is in the order ofO(m). Specifically, the average number of messages is2(n- 1 - γ) -2 ln(n-1) +mat most, in whichγis the Euler constant. To our best knowledge, this is the first contribution that possesses the distributed and scalable properties for the min-max multicast problem and is especially desirable to the large-scale resource-limited multihop wireless networks, like sensor networks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".