Homophily, influence and the decay of segregation in self-organizing networks
Bibliographic record
Abstract
Abstract We study the persistence of network segregation in networks characterized by the co-evolution of vertex attributes and link structures, in particular where individual vertices form linkages on the basis of similarity with other network vertices (homophily), and where vertex attributes diffuse across linkages, making connected vertices more similar over time (influence). A general mathematical model of these processes is used to examine the relative influence of homophily and influence in the maintenance and decay of network segregation in self-organizing networks. While prior work has shown that homophily is capable of producing strong network segregation when attributes are fixed, we show that adding even minute levels of influence is sufficient to overcome the tendency towards segregation even in the presence of relatively strong homophily processes. This result is proven mathematically for all large networks and illustrated through a series of computational simulations that account for additional network evolution processes. This research contributes to a better theoretical understanding of the conditions under which network segregation and related phenomenon—such as community structure—may emerge, which has implications for the design of interventions that may promote more efficient network structures.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".