An efficient distributed cycle/knot detection algorithm
Bibliographic record
Abstract
The purpose of the paper is to present an efficient distributed cycle/knot detection algorithm for general graphs which will determine whether a given node is a member of a knot or a cycle. This is relevant in an application such as parallel simulation in which (1) cycles and knots can arise frequently, (2) the size of the graph is very large and (3) it is necessary to know if a given node is in a cycle or a knot. The algorithm is based on a diffusing computation. It requires less communication cost than preceding algorithms, and is the first algorithm capable of detecting both cycles and knots. The algorithm differs from the classical diffusing computation methods through its use of incomplete search messages to speed up the computation. The algorithm requires a total of at most 2m messages, where m is the number of links. This is compared to Chandy-Misra's algorithm [1983] which requires at least (3m+n), where n is a number of nodes and m is the number of links. The algorithm requires O(log(n)) bits of memory.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".