Statistical Behavior of Embeddedness and Communities of Overlapping\n Cliques in Online Social Networks
Bibliographic record
Abstract
Degree distribution of nodes, especially a power law degree distribution, has\nbeen regarded as one of the most significant structural characteristics of\nsocial and information networks. Node degree, however, only discloses the\nfirst-order structure of a network. Higher-order structures such as the edge\nembeddedness and the size of communities may play more important roles in many\nonline social networks. In this paper, we provide empirical evidence on the\nexistence of rich higherorder structural characteristics in online social\nnetworks, develop mathematical models to interpret and model these\ncharacteristics, and discuss their various applications in practice. In\nparticular, 1) We show that the embeddedness distribution of social links in\nmany social networks has interesting and rich behavior that cannot be captured\nby well-known network models. We also provide empirical results showing a clear\ncorrelation between the embeddedness distribution and the average number of\nmessages communicated between pairs of social network nodes. 2) We formally\nprove that random k-tree, a recent model for complex networks, has a power law\nembeddedness distribution, and show empirically that the random k-tree model\ncan be used to capture the rich behavior of higherorder structures we observed\nin real-world social networks. 3) Going beyond the embeddedness, we show that a\nvariant of the random k-tree model can be used to capture the power law\ndistribution of the size of communities of overlapping cliques discovered\nrecently.\n
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".