Core-sorted heavy-edge matching algorithm based on compressed storage format of graph
Bibliographic record
Abstract
During the coarsening phase of multilevel method,this paper introduces the concept of core and proposes the Core-Sorted Heavy-Edge Matching(CSHEM) algorithm in accordance with the compressed storage format of graph.The CSHEM algorithm not only improves previous matching schemes which are based on local information of vertex,using the global information of the finest graph core to develop its guidance role,but overcomes the defect that can only choose the Random Matching(RM) algorithm as a guide matching algorithm.Furthermore,it also presents an effective matching-based coarsening scheme that uses the CSHEM algorithm on the finest graph and the Sorted Heavy-Edge Matching(SHEM) algorithm on the coarser graphs.The scheme plays a guidance role of the core so as to make the coarser graph in accordance with the core-consistent principle.The experiment and the analysis based on ISPD98 circuit benchmark show the scheme produces encouraging performance improvement compared with those produced by the combination scheme of RM and SHEM of MeTiS that is a state-of-the-art partitioner in the literature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".