A Deeper Dive into Pattern-Aware Subgraph Exploration with PEREGRINE
Bibliographic record
Abstract
Graph mining workloads aim to extract structural properties of a graph by exploring its subgraph structures. PEREGRINE is a general-purpose graph mining system that provides a generic runtime to efficiently explore subgraph structures of interest and perform various graph mining analyses. It takes a 'pattern-aware' approach by incorporating a pattern-based programming model along with efficient pattern matching strategies. The programming model enables easier expression of complex graph mining use cases and enables PEREGRINE to extract the semantics of patterns. By analyzing the patterns, PEREGRINE generates efficient exploration plans which it uses to guide its subgraph exploration. In this paper, we present an in-depth view of the patternanalysis techniques powering the matching engine of PEREGRINE. Beyond the theoretical foundations from prior research, we expose opportunities based on how the exploration plans are evaluated, and develop key techniques for computation reuse, enumeration depth reduction, and branch elimination. Our experiments show the importance of patternawareness for scalable and performant graph mining where the presented new techniques speed up the performance by up to two orders of magnitude on top of the benefits achieved from the prior theoretical foundations that generate the initial exploration plans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".