Bibliographic record
Abstract
We define two novel iterative models of social networks. The models are deterministic processes that generate graphs over discrete time-steps, and the properties of these graphs will be explored. The first model generalizes two known models: the Iterated Local Tran- sitivity Model and the Iterated Local Anti-Transitivity Model. The Iterated Local Model includes as input an infinite binary sequence that determines the way the graphs are constructed over time. These models each utilize the underlying graph structure in previous time-steps. Subsequently, we define a model that is independent of the structure of the graph at the previous time-step. The Iterated Global Model creates new adjacencies based on subsets of vertices of a prescribed cardinality. We prove complex network properties of the Iterated Local Model, such as the small- world property and bad spectral expansion. We also present graph-theoretic properties of the model, such as bounds on the chromatic number, domination number, and Hamiltonicity properties. Analogously, for the Iterated Global Model, we prove both complex network and graph-theoretic properties.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".