Tracking changes in dynamic information networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social network analysis is a discipline that has emerged to analyze social structures and information networks to uncover patterns of interaction among the vertices in the network. Most social networks are dynamic, and studying the evolution of these networks over time could provide insight into the behavior of individuals expressed by the nodes in the graph and the flow of information among them. In a dynamic network, communities, which are groups of densely interconnected nodes, are affected by changes in the underlying population. The analysis of communities and their evolutions can help determine the shifting structural properties of the networks. We present a framework for modeling and detecting community evolution over time. First, our proposed community matching algorithm efficiently identifies and tracks similar communities over time. Then, a series of significant events and transitions are defined to characterize the evolution of networks in terms of its communities and individuals. We also propose two metrics called stability and influence metrics to describe the active behavior of the individuals. We present experiments to explore the dynamics of communities on the Enron email and DBLP datasets. Evaluating the events using topics extracted from the detected communities demonstrates that we can successfully track communities over time in real datasets.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it