Balanced Graph Partitioning: Optimizing graph cut based on Label Swapping
Why this work is in the frame
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Bibliographic record
Abstract
Balanced Graph Partitioning is one of the fundamental combinatorial optimization problems. It is still a challenge to effectively achieve a High-quality Balanced Graph Partitioning for super-large graphs. In this paper, we propose a graph partitioning algorithm based on Label Swapping. Normalized Cut is used as optimization target and this algorithm iteratively updates the graph with Label Swapping. Specifically, by using sampling methods, our method can deal with super-large graph and increase the algorithm's efficiency. To improve the partition's quality, we further propose a variable neighborhood search(VNS) in our algorithm to escape the local optimum. Our experimental results on real-world datasets have shown that the partition's intra-cluster density is very good and and our algorithm outperforms METIS in term of minimum cut.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it