Indexing Techniques for Graph Reachability Queries
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Bibliographic record
Abstract
We survey graph reachability indexing techniques for efficiently processing reachability queries in two popular graph models: plain graphs and edge-labeled graphs . Reachability queries determine whether a directed path exists between a source and a target vertex, forming a core class of navigational queries in graph analytics. Reachability indexes are specialized data structures that accelerate such query processing. Work on this topic goes back four decades—we include 33 of the proposed techniques. Plain graphs consist of only vertices and edges, with reachability queries checking for the existence of a path. Edge-labeled graphs extend plain graphs by adding labels to edges, and their queries further impose constraints on the labels along the path. We categorize indexing techniques for both plain and edge-labeled graphs and discuss them based on this classification, using representative methods to illustrate key ideas. We discuss the main challenges within each category and how these might be addressed in other approaches. We conclude with a discussion of the open challenges and future research directions, along the lines of integrating reachability indexes into modern graph database management systems. This survey serves as a comprehensive resource for researchers and practitioners interested in the advancements, techniques, and challenges of reachability indexing in graph analytics.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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