Experimental Analysis of Streaming Algorithms for Graph Partitioning
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Bibliographic record
Abstract
We report a systematic performance study of streaming graph partitioning algorithms. Graph partitioning plays a crucial role in overall system performance as it has a significant impact on both load balancing and inter-machine communication. The streaming model for graph partitioning has recently gained attention due to its ability to scale to very large graphs with limited resources. The main objective of this study is to understand how the choice of graph partitioning algorithm affects system performance, resource usage and scalability. We focus on both offline graph analytics and online graph query workloads. The study considers both edge-cut and vertex-cut approaches. Our results show that the no partitioning algorithms performs best in all cases, and the choice of graph partitioning algorithm depends on: (i) type and degree distribution of the graph, (ii) characteristics of the workloads, and (iii) specific application requirements.
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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 | 0.000 | 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 | 0.000 | 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