Benchmark Sets and Experimental Results for "Scalable High-Quality Hypergraph Partitioning"
Bibliographic record
Abstract
set_A_M_HG.tar.xz: benchmark set of 488 medium sized hypergraphs. Referenced either as set A or as set M_HG in our publications. HMetis format set_B_L_HG.tar.xz: benchmark set of 94 large hypergraphs. Referenced either as set B or as set L_HG in our publications. HMetis format set_C_M_G.tar.xz: benchmark set of 172 medium sized graphs. Referenced as set M_G in our publications. Metis format set_D_L_G.tar.xz: benchmark set of 53 large graphs. Referenced as set L_G in our publications. Metis format set_A_M_HG.csv: general statistics on the benchmark set of medium sized hypergraphs set_B_M_HG.csv: general statistics on the benchmark set of large hypergraphs set_C_M_G.csv: general statistics on the benchmark set of medium sized graphs set_D_L_G.csv: general statistics on the benchmark set of large graphs results_talg.zip: experimental results for our journal article "Scalable High-Quality Hypergraph Partitioning" (ACM Transactions on Algorithms, 2024) results_alenex21.zip: experimental results for our paper "Scalable Shared-Memory Hypergraph Partitioning" (ALENEX 2021) results_alenex22.zip: experimental results for our paper "Shared-Memory n-level Hypergraph Partitioning" (ALENEX 2022) results_sea22.zip: experimental results for our paper "Parallel Flow-Based Hypergraph Partitioning" (SEA 2022)
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.064 | 0.032 |
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".