Task-Specific Trust Prediction with GNN for Minimized Risk of Task Completion in Dynamic Collaborative Systems
Bibliographic record
Abstract
Complex task completion in resource-constrained Internet of Things (IoT) systems relies exclusively on trusted collaboration among distributed devices. However, dynamic task requirements and collaborator features inevitably introduce collaboration uncertainties, thus increasing task completion risks. To minimize such risks, we propose TST-GNN, a Task-Specific Trust prediction method based on Graph Neural Networks (GNN) to construct reliable collaborator groups for task completion with reduced collaboration uncertainties. Specifically, the collaboration uncertainties are quantified in this paper by our proposed risk assessment metric, i.e., Risk of Task Completion. Then an optimization problem for reliable collaborator selection is formulated to minimize the long-term risk of task completion while completing various tasks with diverse goals. To solve this problem, we first model the dynamic collaborative system as a set of trust-guided bipartite graphs. Then a task-specific trust evaluation model is trained based on GNN for accurate trust prediction and reliable collaborator selection, where the excessive training overhead is reduced by only training the graph knowledge from the most relevant tasks. Moreover, a risk-aware trust update policy is proposed to continuously validate and refine the collaborator selection strategy for effective risk control. Simulation results demonstrate the effectiveness and robustness of the proposed strategy in concurrently reducing the long-term risk of task completion and completing complex tasks.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".