LMST-based searching and broadcasting algorithms over Internet graphs and peer-to-peer computing systems
Bibliographic record
Abstract
In a broadcasting problem, a message is sent from a source to all the other nodes in the network. Blind flooding is a classical mechanism for broadcasting, where each node retransmits received message to all its neighbors. Despite its important advantages, an increase in the number of requests or the size of the routing area produces communication overheads that limit the scalability of blind flooding, especially in networks with dynamic topologies. Theoretically optimal solution is based on minimal spanning trees (MST), but its construction is expensive. Protocols based on local knowledge are recently proposed. In weighted RNG, messages are forwarded only on links which are not the 'longest' in any triangle. In weighted RNGQ, messages are forwarded to links which are not the longest in any triangle or quadrangle. In this paper, we propose weighted LMST as the new graph structure, and apply it for broadcasting. Each node constructs weighted MST based on its 2-hop knowledge. Weighted LMST preserves only edges that are selected by both endpoints. Any available metric, such as delay, can be used as weight. weighted RNG was shown in a previous work to perform better than existing flooding and rumor mongering (or gossip) schemes. The new parameterless weighted LMST scheme is compared to the weighted MST, RNG and RNGQ methods, showing its superiority among localized schemes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".