New Aggregators for Global Reputation on Bi-Lattice Based Trust Model
Bibliographic record
Abstract
Within large and growing human communities where interactions occur, trust is a key factor to consider. Computational trust models have then been widely studied since the 2000s targeting items ratings (e.g. in e-commerce) or M2M (e.g. in IoT network). Among these models, EigenTrust is today one of the most popular and studied ones. It provides a global reputation calculation and is efficient in distributed networks, but not fully satisfactory for human interactions. On the opposite, the Bi-lattice model is well suited for human networks interactions such as solidarity networks and/or human services exchange networks but is limited to local trust results. In this paper, we propose a new aggregator that extends the Bi-lattice model to enable a global reputation calculation. This new aggregator discovers the trust links from the member whose score is to be evaluated to every other members he is connected to on the trust network. It then computes the global reputation of this member based on these trust links. Furthermore, it enables a lightweight approach, as it is able to compute a global reputation based only on a partial knowledge of the trust network. Throughout the paper, the proposed aggregator is presented, evaluated and compared to Eigentrust to show its effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".