Assessing the Significance of Data Mining Results on Graphs with Feature Vectors
Bibliographic record
Abstract
Assessing the significance of data mining results is an important step in the knowledge discovery process. While results might appear interesting at a first glance, they can often be explained by already known characteristics of the data. Randomization is an established technique for significance testing, and methods to assess data mining results on vector data or network data have been proposed. In many applications, however, both sources are simultaneously given. Since these sources are rarely independent of each other but highly correlated, naively applying existing randomization methods on each source separately is questionable. In this work, we present a method to assess the significance of mining results on graphs with binary features vectors. We propose a novel null model that preserves correlation information between both sources. Our randomization exploits an adaptive Metropolis sampling and interweaves attribute randomization and graph randomization steps. In thorough experiments, we demonstrate the application of our technique. Our results indicate that while simultaneously using both sources is beneficial, often one source of information is dominant for determining the mining results.
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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 | 0.026 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".