Joint Partitioning and Sampling Algorithm for Scaling Graph Neural Network
Bibliographic record
Abstract
Graph Neural Network (GNN) has emerged as a popular toolbox for solving complex problems on graph data structures. Graph neural networks use machine learning techniques to learn the vector representations of nodes and/or edges. Learning these representations demands a huge amount of memory and computing power. The traditional shared-memory multiprocessors are insufficient to meet real-world data’s computing requirements; hence, research has gained momentum toward distributed GNN.Scaling the distributed GNN has the following challenges: (1) the input graph needs to be efficiently partitioned, (2) the cost of communication between compute nodes should be reduced, and (3) the sampling strategy should be efficiently chosen to minimize the loss in accuracy. To address these challenges, we propose a joint partitioning and sampling algorithm, which partitions the input graph with weighted METIS and uses a bias sampling strategy to minimize total communication costs.We implemented our approach using the DistDGL framework and evaluated it using several real-world datasets. We observe that our approach (1) shows an average reduction in communication overhead by 53%, (2) requires less partitioning time to partition a graph, (3) shows improved accuracy, (4) shows a speed up of 1.5x on OGB-Arxiv dataset, when compared to the state-of-the-art DistDGL implementation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".