DISTINGER: A distributed graph data structure for massive dynamic graph processing
Bibliographic record
Abstract
Large and dynamic graphs with streaming updates have been gaining traction recently, along with the need for enabling graph analytics in a commodity cluster instead of a high-performance computing facility. Surprisingly, there is a lack of study on scaling out graph data structures to represent sparse dynamic graphs in a commodity cluster, and even the latest work [1] based upon the most common in-memory graph representation CSR [2] is a single-machine case. In this paper we present DISTINGER, a distributed graph representation that handles massive graph analytics with streaming updates. DISTINGER successfully extends a scale-up design to a scale-out graph data structure while maintains its efficiency and scalability. We implement our design and algorithms as a prototype, and compare it to single-site STINGER and state-of-art graph systems. Our experimental evaluation in a real cluster shows that DISTINGER can handle larger graphs than STINGER, and perform graph tasks (PageRank and edge updates) more efficiently than GraphLab and Giraph.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 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".