PruneJuice: pruning trillion-edge graphs to a precise pattern-matching solution
Bibliographic record
Abstract
Pattern matching is a powerful graph analysis tool. Unfortunately, existing solutions have limited scalability, support only a limited set of search patterns, and/or focus on only a subset of the real-world problems associated with pattern matching. This paper presents a new algorithmic pipeline that: (i) enables highly scalable pattern matching on labeled graphs, (ii) supports arbitrary patterns, (iii) enables trade-offs between precision and time-to-solution (while always selecting all vertices and edges that participate in matches, thus offering 100% recall), and (iv) supports a set of popular data analytics scenarios. We implement our approach on top of HavoqGT and demonstrate its advantages through strong and weak scaling experiments on massive-scale real-world (up to 257 billion edges) and synthetic (up to 4.4 trillion edges) graphs, respectively, and at scales (1,024 nodes / 36,864 cores) orders of magnitude larger than used in the past for similar problems.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".