Thrifty Label Propagation: Fast Connected Components for Skewed-Degree Graphs
Bibliographic record
Abstract
Various concurrent algorithms have been proposed in the literature in recent years that mostly focus on the disjoint set approach to the Connected Components (CC) algorithm. However, these CC algorithms do not take the skewed structure of real-world graphs into account and as a result they do not benefit from common features of graph datasets to accelerate processing.We investigate the implications of the skewed degree distribution of real-world graphs on their connectivity and we use these features to introduce Thrifty Label Propagation as a structure-aware CC algorithm obtained by incorporating 4 fundamental optimization techniques in the Label Propagation CC algorithm.Our evaluation on 15 real-world graphs and 2 different processor architectures shows that Thrifty accelerates the flow of labels and processes only 1.4% of the edges of the graph.In this way, Thrifty is up to 16 × faster than state-of-the-art CC algorithms such as Afforest, Jayanti-Tarjan, and Breadth-First Search CC. In particular, Thrifty delivers 1.5 × −19.9× speedup for graph datasets larger than one billion edges.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".