Discovering top-k teams of experts with/without a leader in social networks
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Simulation or modelingConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.668
- Threshold uncertainty score
- 0.320
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
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 | 0.000 | 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)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.205 · how far apart the two teachers sit on this one work
- Validation status
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
Abstract
We study the problem of discovering a team of experts from a social network. Given a project whose completion requires a set of skills, our goal is to find a set of experts that together have all of the required skills and also have the minimal communication cost among them. We propose two communication cost functions designed for two types of communication structures. We show that the problem of finding the team of experts that minimizes one of the proposed cost functions is NP-hard. Thus, an approximation algorithm with an approximation ratio of two is designed. We introduce the problem of finding a team of experts with a leader. The leader is responsible for monitoring and coordinating the project, and thus a different communication cost function is used in this problem. To solve this problem, an exact polynomial algorithm is proposed. We show that the total number of teams may be exponential with respect to the number of required skills. Thus, two procedures that produce top-k teams of experts with or without a leader in polynomial delay are proposed. Extensive experiments on real datasets demonstrate the effectiveness and scalability of the proposed methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Topic
- Mobile Crowdsensing and Crowdsourcing
- Field
- Computer Science
- Canadian institutions
- York University
- Funders
- not available
- Keywords
- ScalabilityComputer scienceSet (abstract data type)Function (biology)Approximation algorithmPolynomialSocial network (sociolinguistics)Mathematical optimizationArtificial intelligenceAlgorithmMathematicsSocial mediaWorld Wide Web
- Has abstract in OpenAlex
- yes