Giant cluster formation and integrating role of bridges in social diffusion
Bibliographic record
Abstract
Abstract Research Summary In social networks, isolated subgroups often aggregate into a massively connected subgroup, or a giant cluster, when bridges are built across subgroups. To understand the roles of bridges in integrating subgroups, we develop models focusing on the percentage of bridges among all ties. When it is below 1%, diffusion does not affect many individuals because the system is merely a collection of fragmented subgroups. Near 1%, however, we find that a slight increase in the percentage of bridges leads to sudden widespread diffusion across many subgroups. This dramatic change stems from a threshold‐like structural characteristic of the network whereby previously fragmented subgroups come together abruptly. Our findings suggest that this integrating role of bridges is an important piece missing from the literature on small‐world networks. Managerial Summary Our findings suggest that the formation of a giant cluster could be a structural precondition for large‐scale diffusion. Detection of such clusters may allow prognostication of the possibility of large‐scale diffusion. With the rise of social media and the availability of large amounts of social network data, the ability to detect giant clusters seems to be more attainable than in the past; such an ability would be a source of competitive advantage. We describe methods of detecting giant clusters and analyzing their structural properties using readily available social network data. With these methods, entrepreneurs and established firms can stimulate user adoption by targeting massive clusters of aggregated subgroups and spreading viral messages about their new products or services throughout the clusters.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".