Gauging node consistency in accusation–endorsement networks
Bibliographic record
Abstract
Abstract Many signed, directed social networks can be viewed as being composed of positive (endorsements) and negative (accusations) directed edges, and these networks can in turn be created through a variety of different processes. The recently proposed consistency dynamics supposes that when nodes expect to be judged based on their associations in the network, they may create edges out of a desire to appear as having consistent judgements. We develop a quantifiable score that can rate the level of consistency in a node’s judgement. We demonstrate that this consistency score can be efficiently estimated using a modification of the popular personalized PageRank algorithm and evaluate the score’s properties. In order to validate this score’s relevance to empirical networks, we use consistency scores to perform an edge prediction task, and demonstrate that it performs competitively with, and adds complementary information to, more complicated measures designed specifically for that task. We also demonstrate that the nodes in these networks exhibit specific behaviours that consistency can identify across a range of parameterization values and which are not recoverable by other measures in isolation.
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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 | 0.006 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".