Analyzing time-aggregated networks: the role of bootstrapping, permutation, and simulation
Bibliographic record
Abstract
Abstract Networks are often used to describe adaptive social systems, where individual (node) behaviour generates network-level structures that influence subsequent individual-level behaviour. To address questions about the dynamics of network structure in these systems, there is a need to analyze networks through time. Various statistical methods exist for estimating the behaviour of networks in time, in terms of both time-ordered and time-aggregated networks. In this paper, we discuss three main analytical steps for the analysis of time-aggregated network data: 1) aggregation choices, 2) null-model comparisons, and 3) constructing, parameterizing, and making inferences from time series models. We then present a custom R package, netTS, which facilitates these steps. Observed grooming data from a group of vervet monkeys, a highly social primate species, is used as an example to highlight three potential analyses: 1) quantifying the stability of network-level social structures through time, 2) identifying keystone nodes driving/maintaining network structures, and 3) quantifying the interdependence between node behaviour through time. In particular, we highlight the role of bootstrapping, permutation, and simulation as critical components in the analysis of time-aggregated networks.
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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 | 0.027 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".