Two-Phase Pareto Set Discovery for Team Formation in Social Networks
Bibliographic record
Abstract
In this paper, we study the problem of finding teams of experts from an expert network while optimizing three objectives. Given a project, the objective is to find teams of experts that cover all the required skills and also optimize the communication cost as well as the personnel cost and the expertise level of the team members. The expert network is modeled as a graph, where nodes represent experts and edges between nodes specify the communication costs between the experts. In this paper, we are interested in finding a Pareto front of teams that not only cover the required skills but are also not dominated by other feasible teams with respect to the three criteria. Since the problem is NP-hard, we propose algorithms to use with a two-phase method to find an approximation of the Pareto front for the three criteria team formation problem. In the first phase, an initial population which is composed of an approximation of the supported efficient teams is generated. Then, a Pareto local search method is applied to each solution of the initial population to find other members of the Pareto front. The proposed method is evaluated on the DBLP data set. The results indicate its superior performance comparing with other methods in terms of running time and the quality of answers.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".