Random graph models for temporal processes in social networks
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Bibliographic record
Abstract
We generalize the graphical modeling approach of p* social influence models to develop discrete time models for the temporal evolution of social networks. Plausible general processes pertaining to network evolution are broadly discussed as a basis for across‐time dependence assumptions. Systematic temporal processes are construed as effects that are homogeneous across the network, and that reflect dynamics inherent in a particular social relation. Any one actor cannot control these dynamics, especially given that non‐dyadic configurations may be implicated, for instance, tendencies for various triadic configurations to be constructed or to collapse of over time. Non‐systematic processes, on the other hand, may pertain to the changing nature of a particular dyadic tie, or to change involving a particular sociotemporal neighborhood of the network. Non‐systematic processes are inhomogeneous across time and across the network, and are modeled as random. In constructing p* dependence graphs, systematic temporal processes may be represented, in part, by the perfect dependence assumption, whereby network across‐time dependencies "mirror" within‐time dependencies. We develop temporal perfect dependence models appropriate for Markov random graphs. To disentangle non‐systematic from systematic temporal processes is not straightforward, but the use of the constant tie assumption ‐ whereby ephemeral ties are assumed not to have influence across time ‐is one possible approach. We illustrate these models with three empirical examples: first, with an analysis of the Freeman EIES data; and then with data from a newly formed small training group involving two networks, trust and friendship. Notes An earlier version of this paper was presented at the Sunbelt International Social Networks meeting, Vancouver, April 2000. The authors would like to thank Tom Snijders and Peter Elliott for helpful comments on this paper. Corresponding author. E‐mail: g.robins@psych.unimelb.edu.au.
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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) | 0.001 | 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) | 0.000 | 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