Subgraph Representation Learning for Team Mining
Why this work is in the frame
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Bibliographic record
Abstract
Team mining is concerned with the identification of a group of experts that are able to collaborate with each other in order to collectively cover a set of required skills. This problem has mainly been addressed either through graph search, which looks for subgraphs that satisfy the skill requirements or through neural architectures that learn a mapping from the skill space to the expert space. An exact graph-based solution to this problem is intractable and its heuristic variants are only able to identify sub-optimal solutions. On the other hand, neural architecture-based solutions are prone to overfitting and simplistically reduce the problem of team formation to one of expert ranking. Our work in this paper proposes an unsupervised heterogeneous skip-gram-based subgraph mining approach that can learn representations for subgraphs in a collaboration network. Unlike previous work, the subgraph representations allow our method to mine teams that have past collaborative history and collectively cover the requested desirable skills. Through our experiments, we demonstrate that our proposed approach is able to outperform a host of state-of-the-art team mining techniques from both quantitative and qualitative perspectives.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it