Using graph partitioning for efficient network modularity optimization
Bibliographic record
Abstract
The paper reviews an approach for finding the communities of a network developed by the authors [WAW’06, Lecture Notes in Computer Science, Volume 4936/2008, 117-128, IEEE TPDS vol. PP, issue 99, 2012], which is based on a reduction of the modularity optimization problem to the minimum weighted cut problem, and gives an experimental evaluation of an implementation based on that approach on graphs from the 10th DIMACS Implementation Challenge Testbed. Specifically, we describe a reduction from the problem of finding a partition of the nodes of a graph G G that maximizes the modularity to the problem of finding a partition that minimizes the weight of the cut in a complete graph on the same node set as G G , and weights dependent on a random graph model associated with G G . The resulting minimum cut problem can then be solved by modifying existing codes for graph partitioning. We compare the performance of our implementation based on the Metis graph partitioning tool [SIAM J. Sci. Comp. 20, 359–392] against one of the best performing algorithms described in this volume.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| 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 | 0.002 |
| 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".