Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-Winner Contests for Strategic Diffusion in Social Networks
Authors: Wen Shen, Yang Feng, Cristina V. Lopes6154-6162
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on four real-world social network datasets demonstrate that stakeholders can significantly boost participants aggregated efforts with proper design of competitions. |
| Researcher Affiliation | Academia | Wen Shen, Yang Feng, Cristina V. Lopes University of California, Irvine, California 92697, United States EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Multi-Winner Contests Mechanism |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the described methodology. |
| Open Datasets | Yes | We used four publicly available datasets: Twitter (Hodas and Lerman 2014), Flickr (Cha, Mislove, and Gummadi 2009), Flixster (Goyal, Bonchi, and Lakshmanan 2011), and Digg (Hogg and Lerman 2012). |
| Dataset Splits | No | The paper describes using datasets for simulation and analysis but does not specify training, validation, or test dataset splits (e.g., percentages or counts for each split). |
| Hardware Specification | Yes | We ran all the experiments on the same 3.7GHz 6-core Linux machine with 32GB RAM. |
| Software Dependencies | No | The paper mentions learning algorithms and models but does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their versions). |
| Experiment Setup | Yes | We set λ = 0.5 as it was standard in many geometric reward mechanisms. In practice, a stakeholder usually sets ϕ 1 to make profits, but ϕ should be as close to 1 as possible to encourage players to participate. We let ϕ = 1. To encourage players to join, we set µ = 0.9, and φ = ϕ µ = 0.1. Note that η λ/2 = 0.25, we set η = 0.25. For each group of players in each dataset, we varied the noise factors from 0 to 1 with an increment of 0.05. For each result (i.e., a data point) obtained, we ran the respective experiment 20 times. |