Paradigm Shifts in Team Recommendation: From Historical Subgraph Optimization to Emerging Graph Neural Network
Bibliographic record
Abstract
Collaborative team recommendation involves selecting experts with certain skills to form a team who will, more likely than not, accomplish a task successfully.To automate the traditionally tedious and error-prone manual process of team formation, researchers from several scientific spheres have proposed methods to tackle the problem.In this tutorial, while providing a taxonomy of team recommendation works based on their algorithmic approaches, we foremost perform a comprehensive study of the graph-based approaches that comprise the pioneering works in this field, then cover the graph neural network-based studies as the cutting-edge class of approaches.Further, we provide unifying definitions, formulations, and evaluation schema along with the details of training strategies, benchmarking datasets, useful open-source tools and performance comparison of the works.Finally, we identify directions for future works.Our tutorial and materials are available at https://fani-lab.github.io/OpeNTF/tutorial/sigir-ap24/.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".