Constructing and analysing time‐aggregated networks: The role of bootstrapping, permutation and simulation
Bibliographic record
Abstract
Abstract Animal social networks are often used to describe dynamic social systems, where individual behaviour generates network‐level structures that subsequently influence individual‐level behaviour. This interdependence between individual behaviour and group structuring is of central concern for questions concerning the evolution and development of social systems and collective animal behaviour more generally. Various statistical methods exist for estimating network changes through time. One approach, time‐aggregated networks, takes repeated snapshots of interactions within windows of time to generate a time series of networks. However, there remain many analytical hurdles when implementing the time‐aggregated approach. To ameliorate this, we introduce an r package net TS that focuses on three analytical steps for analysing time‐aggregated networks: choosing appropriate time scale using bootstrapping, comparing patterns to relevant null models using permutation and finally building and interpreting statistical models using simulated data. We use simulated data to first highlight these steps, then use observed grooming data from a group of vervet monkeys as an applied example. Our results suggest that the use of bootstrapping and permutation can accurately extract known patterns from simulated data. Using this approach with vervet data suggests that there is consistent social structuring, differing from what would be expected due to chance, and that some individuals are contributing to this structure more than others (i.e. keystone individuals). We demonstrate that bootstrapping, permutation and simulation can aid in constructing and interpreting time‐aggregated networks. We suggest that the use of time‐aggregated networks to quantify patterns of network change can be a useful tool alongside process‐based approaches that seek mechanistic descriptions. Ultimately, by looking at both patterns and processes, dynamic networks can be used to better understand how individual behaviour generates social structures, and in turn how individual behaviour can be influenced by social structures, ultimately leading to a better understanding of the evolution of social behaviour.
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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 | 0.032 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".