Analysis of METIS graph partitioning algorithms for trust and recommendation systems
Bibliographic record
Abstract
In the era where social media and technology intersect, the vast user base of social networks presents a challenge in handling massive data. The issue intensifies when making user suggestions amidst the overwhelming data flow. This analysis addresses the complexities arising from the abundance of data in social networks and proposes a solu- tion through advanced graph partitioning techniques, focusing on algorithms from promi-nent libraries like DGL and PyTorch. This analysis compares three graph partitioning algorithms for social network analysis: DGL METIS (edge-balanced and node-balanced), and PyG METIS. We analyze their performance on the Epinions social recommendation dataset, focusing on edge based and node based metrics and visualization of partitions.Our findings reveal: PYG METIS consistently exhibited suboptimal performance across various evaluation metrics, with the exception of achieving satisfactory results in node balance. Conversely, DGL Node Balanced METIS demonstrated marginally superior outcomes compared to DGL Edge Balanced METIS in terms of edge loss and average edges per partition and surpassed it in node balance.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".