Distributed balanced edge-cut partitioning of large graphs having weighted vertices
Bibliographic record
Abstract
Large scale graphs are sometimes too big to store and process on a single machine. Instead, these graphs have to be divided into smaller parts and distributed over several machines, while minimizing the dependency between the different parts. This is known as the graph partitioning problem, which has been shown to be NP-complete. The problem is well studied, however most solutions are either not suitable for a distributed environment or unable to do balanced partitioning of graphs having weighted vertices. This thesis presents an extension to the distributed balanced graph partitioning algorithm JA-BE-JA, that solves the balanced partitioning problem for graph having weighted vertices. The extension, called wJA-BE-JA, is implemented in both the Spark framework and in Scala. The two main contributions of this report are the algorithm and a comprehensive evaluation of its performance, including a comparison with the recognized METIS graph partitioner. The evaluation shows that a random sampling policy in combination with the simulated annealing technique gives good results. It further shows that the algorithm is competitive to METIS, as the extension outperforms METIS in 17 of 20 tests.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".