A+ Indexes: Tunable and Space-Efficient Adjacency Lists in Graph Database Management Systems
Bibliographic record
Abstract
Graph database management systems (GDBMSs) are highly optimized to perform fast traversals, i.e., joins of vertices with their neighbours, by indexing the neighbourhoods of vertices in adjacency lists. However, existing GDBMSs have system-specific and fixed adjacency list structures, which makes each system efficient on only a fixed set of workloads. We describe a new tunable indexing subsystem for GDBMSs, we call A+ indexes, with materialized view support. The subsystem consists of two types of indexes: (i) vertex-partitioned indexes that partition 1-hop materialized views into adjacency lists on either the source or destination vertex IDs; and (ii) edge-partitioned indexes that partition 2-hop views into adjacency lists on one of the edge IDs. As in existing GDBMSs, a system by default requires one forward and one backward vertex-partitioned index, which we call the primary A+ index. Users can tune the primary index or secondary indexes by adding nested partitioning and sorting criteria. Our secondary indexes are space-efficient and use a technique we call offset lists. Our indexing subsystem allows a wider range of applications to benefit from GDBMSs' fast join capabilities. We demonstrate the tunability and space efficiency of A+ indexes through extensive experiments on three workloads.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".