A Diffusion of Innovation-Based Closeness Measure for Network Associations
Bibliographic record
Abstract
Network association is a prevalent representation when dealing with data from present-day applications. Examples are crime event connections in criminology, cellphone call graphs in telecommunication, co-authorship networks in bibliometrics, etc. A large body of work has been devoted to the analysis of these networks and the discovery of their underlying structures. One important structure is the notion of community i.e. a group of nodes that are relatively cohesive within and reasonably disjointed outside. Finding the communities usually relies on a closeness/distance measure between network nodes. In this paper, we propose a novel closeness measure, named iCloseness, inspired by the theory of Diffusion of Innovations in anthropology. It is computed based on the intersection of neighbourhoods and quantifies the closeness of two nodes. To apply this measure we adjusted the Top Leaders community mining method to use this measure for community detection. Experimental results on real world and synthesized information networks show the effectiveness of our proposed measure and highly motivate the application of the iCloseness measure in the context of community mining.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".