Graph Partitioning and Sparse Matrix Ordering using Reinforcement\n Learning and Graph Neural Networks
Bibliographic record
Abstract
We present a novel method for graph partitioning, based on reinforcement\nlearning and graph convolutional neural networks. Our approach is to\nrecursively partition coarser representations of a given graph. The neural\nnetwork is implemented using SAGE graph convolution layers, and trained using\nan advantage actor critic (A2C) agent. We present two variants, one for finding\nan edge separator that minimizes the normalized cut or quotient cut, and one\nthat finds a small vertex separator. The vertex separators are then used to\nconstruct a nested dissection ordering to permute a sparse matrix so that its\ntriangular factorization will incur less fill-in. The partitioning quality is\ncompared with partitions obtained using METIS and SCOTCH, and the nested\ndissection ordering is evaluated in the sparse solver SuperLU. Our results show\nthat the proposed method achieves similar partitioning quality as METIS and\nSCOTCH. Furthermore, the method generalizes across different classes of graphs,\nand works well on a variety of graphs from the SuiteSparse sparse matrix\ncollection.\n
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".