Vectorising k-Truss Decomposition for Simple Multi-Core and SIMD Acceleration
Bibliographic record
Abstract
In this paper we tackle truss decomposition of large graphs, which is one of the popular tools for discovering dense hierarchical subgraphs in social and web networks; such subgraphs form the basis of community discovery, one of the cornerstones of modern graph analytics. Our goal is to offer a simple vectorisation approach which can be easily implemented in widely popular Python vector libraries, such as NumPy. This way has two advantages: (1) non-experts with basic knowledge of Python can implement our algorithm, and (2) they can obtain multi-threaded and SIMD parallelism “for free” without them needing to know about computer architecture or sophisticated C++ libraries for multi-threaded processing. We believe this is an important paradigm setting approach that opens the way for applying similar techniques to other problems that might seem at first remote to vectorisation and/or parallelisation.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".