Bibliographic record
Abstract
We study the problem of the amount of knowledge about a communication network that must be given to its nodes in order to efficiently disseminate information. While previous results about communication in networks used particular partial information available to nodes, such as the knowledge of the neighborhood or the knowledge of the network topology within some radius, our approach is quantitative: we investigate the minimum total number of bits of information (minimum oracle size) that has to be available to nodes in order to perform efficient communication.It turns out that the minimum oracle size for which a distributed task can be accomplished efficiently, can serve as a measure of the difficulty of this task. We use this measure to make a quantitative distinction between the difficulty of two apparently similar fundamental communication primitives: the broadcast and the wakeup. In both of them a distinguished node, called the source, has a message, which has to be transmitted to all other nodes of the network. In the wakeup, only nodes that already got the source message (i.e., are awake) can send messages to their neighbors, thus waking them up. In the broadcast, all nodes can send control messages even before getting the source message, thus potentially facilitating its future dissemination. In both cases we are interested in accomplishing the communication task with optimal message complexity, i.e., using a number of messages linear in the number of nodes.We show that the minimum oracle size permitting the wakeup with a linear number of messages in a n-node network, is Θ (n log n), while the broadcast with a linear number of messages can be achieved with an oracle of size O(n). We also show that the latter oracle size is almost optimal: no oracle of size o(n) can permit to broadcast with a linear number of messages. Thus an efficient wakeup requires strictly more information about the network than an efficient broadcast.
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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 | 0.008 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.022 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".