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 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 | 0.001 | 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".