An Efficient Algorithm for Answering Graph Reachability Queries
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Bibliographic record
Abstract
Given a directed graph G, to check whether a node v is reachable from another node u through a path is often required. In a database system, such an operation is called a recursion computation or reachability checking and not efficiently supported. The reason for this is that the space to store the whole transitive closure of G is prohibitively high. In this paper, we address this issue and propose an 0(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + bnradic(b)) time algorithm to decompose a directed acyclic graph (DAG) into a minimized set of disjoint chains to facilitate reachability checking, where n is the number of the nodes and b is the DAG's width, defined to be the size of a largest node subset U of the DAG such that for every pair of nodes u, v isin U, there does not exist a path from u to v or from v to u. Using this algorithm, we are able to label a graph in 0(be) time and store all the labels in O(bn) space with O(log <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</i> ) reachability checking time, where e is the number of the edges of the DAG. The method can also be extended to handle cyclic directed graphs. Experiments have been performed, showing that our method is promising.
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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 | 0.001 |
| 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 it