Random graph models for temporal processes in social networks
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.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".