Fast Partitioning for Distributed Graph Learning using Multi-level Label Propagation
Bibliographic record
Abstract
Graph Neural Networks (GNNs) are a popular class of machine learning models that allow scientists to leverage machine learning techniques to perform inference on unstructured data. However, when graphs become too large, partitioning becomes necessary to allow for distributed computation. Standard graph partitioning methods for GNNsinclude Random partitioning and the state-of-the-art METIS. Whereas METIS produces partitions of high-quality, its preprocessing overheads make it impractical for extremely large graphs. Conversely, random partitioning is cheap to compute, but results in poor partition quality that causes GNN training to be bottlenecked by communication. In my thesis, I seek to prove that it is possible to reduce the data preprocessing overhead on small machines for large graph datasets used in ML while maintaining partition quality. In support of this goal, I design and implement a hierarchical label-propagation-based graph partitioning system known as PLaTE (Propagating Labels to Train Efficiently), partially based on the paper “How to Partition a Billion Node Graph” [18]. PLaTE runs 5.6x faster than METIS on the Open Graph Benchmark’s papers100M dataset, while consuming 4.9x less memory. PLaTE produces partitions that are equally balanced to METIS with comparable communication volumes under certain conditions. In real GNN training experiments, PLaTE has comparable average epoch times to METIS.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".