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..
Multistage Campaigning in Social Networks
Authors: Mehrdad Farajtabar, Xiaojing Ye, Sahar Harati, Le Song, Hongyuan Zha
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic data and the real-world Meme Tracker dataset show that our algorithm can steer the user activities for optimal campaigning much more accurately than baselines. |
| Researcher Affiliation | Academia | Mehrdad Farajtabar Xiaojing Ye Sahar Harati Le Song Hongyuan Zha Georgia Institute of Technology Georgia State University Emory University EMAIL EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1: Closed-loop Multi-stage Dynamic Programming |
| Open Source Code | Yes | codes are available at http://www.cc.gatech.edu/~mfarajta/ |
| Open Datasets | Yes | We utilize the Meme Tracker dataset [9] which contains the information flows captured by hyperlinks between different sites with timestamps during 9 months. This data has been previously used to validate Hawkes process models of social activity [5, 10]. [9] J Leskovec, L Backstrom, and J Kleinberg. Meme-tracking and the dynamics of the news cycle. SIGKDD, 2009. |
| Dataset Splits | No | The paper mentions using synthetic and real-world data, but does not specify explicit training, validation, and test dataset splits (e.g., percentages, sample counts, or k-fold cross-validation details) for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks used in the implementation. |
| Experiment Setup | Yes | Details of the experimental setup and parameter setting are found in appendix F. |