A Survey of Subgraph Optimization for Expert Team Formation
Bibliographic record
Abstract
Expert Team Formation is the search for gathering a team of experts who are expected to collaboratively work toward accomplishing a given project, a problem that has historically been solved in a variety of ways, including manually in a time-consuming and bias-filled manner, and algorithmically within disciplines like social sciences and management. In the present effort, while providing a taxonomy to distinguish between search-based versus learning-based approaches, we survey graph-based studies from the search-based category, motivated as they comprise the mainstream. We present a unifying and vetted overview of the various definitions in this realm, scrutinize assumptions, and identify shortfalls. We start by reviewing initial approaches to the Expert Team Formation problem to lay the conceptual foundations and set forth the necessary notions for a more grounded view of this realm. Next, we provide a detailed view of graph-based Expert Team Formation approaches based on the objective functions they optimize. We lay out who builds on whom and how algorithms have evolved to solve the drawbacks of previous works. Furthermore, we categorize evaluation schemas and elaborate on metrics and insights that can be drawn from each. Referring to the evaluation schemas and metrics, we compare works and propose future directions.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".