Bibliographic record
Abstract
A graph reachability query, as one of the primary tasks in numerous applications, is to find whether two given data objects, u and v, are related in any way in a large and complex dataset. Formally, the query is about to find if v is reachable from u in a directed graph which is large in size. In this paper, we focus ourselves on building a reachability labeling for large directed graphs, in order to process reachability queries efficiently. A new approach is proposed to compress transitive closure to support reachability checkings. The approach consists of two schemes, called Core-I labeling and Core-II labeling, respectively. For a graph G with n nodes and e edges, the labeling time of Core-I is bounded by O(n + e + t¿min{b, s}), where b is the number of the leaf nodes of a spanning tree of G, t is the number of non-tree edges (edges that do not appear in the spanning tree) and s is the number of the start nodes of all non-tree edges in G. The space overhead is bounded by O(n + s¿min{b, s}) and the querying time is O(log(min{b, s})). Core-II needs O(n + e + t¿min{b, s} + d¿s¿logmin{b, s}) labeling time and O(n + d¿s) space, where d is the number of the end nodes of all non-tree edges in G. But the query time is reduced to O(1). Experiments have been performed, showing that our method is promising.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".