SSRM: structural social role mining for dynamic social networks
Bibliographic record
Abstract
A social role is a special position an individual possesses within a network, which indicates his or her behaviours, expectations, and responsibilities. Identifying the roles that individuals play in a social network has various direct applications, such as detecting influential members, trustworthy people, idea innovators, etc. Roles can also be used for further analyses of the network, e.g. community detection, temporal event prediction, and summarization. In this paper, we propose a structural social role mining framework (SSRM), which is built to identify roles, study their changes, and analyze their impacts on the underlying social network. We define fundamental roles in a social network (namely leader, outermost, mediator, and outsider), and then propose methodologies to identify them, and track their changes. To identify these roles, we leverage the traditional social network analyses and metrics, as well as proposing new measures, including community-based variants for the Betweenness centrality. Our results indicate how the changes in the structural roles, in combination with the changes in the community structure of a network, can provide additional clues into the dynamics of networks.
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How this classification was reachedexpand
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".