Multistage Campaigning in Social Networks
Authors: Mehrdad Farajtabar, Xiaojing Ye, Sahar Harati, Le Song, Hongyuan Zha
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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 mehrdad@gatech.edu xye@gsu.edu sahar.harati@emory.edu {lsong,zha}@cc.gatech.edu |
| 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. |