A Graph-based Analysis Approach to Cluster Lifetime Dynamics
Why this work is in the frame
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Bibliographic record
Abstract
Spatial-temporal data analysis helps uncover value from data that moves through space and time. One such data analysis technique is clustering, which groups data based on a distance function to identify outliers or assist in classification tasks. Once spatial-temporal data is clustered with respect to space and time, cluster relationships can be observed, such as clusters entering or leaving another, merging, or splitting. A cluster lifetime describes the relationships that a given cluster had from its start to finish. The set of all cluster lifetimes that are related by the relationships describe a cluster dynamic. In this paper, we report our work in progress on a graph-based analysis approach to cluster lifetime dynamics. We discuss how cluster dynamics can be represented using graphs and the opportunities resulting from this approach, including visualization, graph pattern mining, graph classification, and graph compression. Enabled by graph-processing techniques, the proposed approach facilitates tasks such as the detection of regions of significant increase or decrease in the number of cluster elements (e.g. traffic jams), the calculation of a rise or decay parameter to describe this behavior for classification or comparison tasks, and the identification of a cluster’s lifetime, direction, and distance from or to a given point of interest.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.021 | 0.013 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it