Bibliographic record
Abstract
The proliferation in size of actual graph datasets impels the use of distributed graph processing frameworks which in turn, should consider a good partitioning of the graph dataset in order to see their performances enhanced. In this paper, we focus on a well known heuristic for graph partitioning named METIS, an offline method giving high quality partitions but unsuitable for processing large graphs due to the offline setting. A recently proposed alternative is the streaming partitioning heuristics aiming to alleviate the computational resources constraints when dealing with large graphs. In considering this matter, we propose a new partitioning method that benefits from the accuracy of METIS and the lightness of the streaming setting. This work introduces the Streaming METIS Partitioning method (SMP) which is an online counterpart of METIS, a fast and well known multilevel heuristic for graph partitioning. We show in a complexity analysis that SMP has a lower time complexity compared to METIS, which is confirmed by conducted experiments. Moreover, we show that SMP yields competitive results to its offline counterpart METIS, especially when it is run on a Depth First Search streaming order. Also, when compared to other online competitors, SMP is the best performing heuristic giving partitions with minimal edge cut.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".