Positive Communities on Signed Graphs That Are Not Echo Chambers: A Clique-Based Approach
Bibliographic record
Abstract
An area of research on communities in signed networks aims to find structures in which each user in the graph is connected to other members in their community by more positive edges than negative edges, indicating a positive experience for the user. However, some of these communities are ‘echo chambers', a rising area of concern in modern discourse regarding social media, which contain almost exclusively positive edges indicating all users trust each other with little or no push-back. Here exists an interesting contradiction, when finding a ‘positive’ community often times the resulting structure may be the negative ‘echo chamber’. In this work we propose a signed graph community substructure named the$(\epsilon,\ \phi)$-Clique which is the best of both worlds, where each user is happy to be in their community (indicated by have a proportion of positive edges$\geq\epsilon$for each node) as well as there existing a level of disagreement in the system (indicated by the community having a proportion of negative edges$\geq \phi$). From this definition, we design algorithms to exactly find the Maximum$(\epsilon,\ \phi)$-Clique containing a query user, utilising heuristics to combat the NP-Hard and NP-Hard to approximate nature of the problem. We perform experiments to examine the improvements in efficiency of our algorithms to the proposed baseline as well as examine example community outputs to show the effectiveness of our structure.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".