Shaping Social Activity by Incentivizing Users
Authors: Mehrdad Farajtabar, Nan Du, Manuel Gomez Rodriguez, Isabel Valera, Hongyuan Zha, Le Song
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives.We also conducted experiments on a network of 60,000 Twitter users and more than 7,500,000 uses of a popular url shortening services. Using held-out data, we show that our algorithm can shape the network behavior much more accurately than alternatives. |
| Researcher Affiliation | Academia | Georgia Institute of Technology MPI for Software Systems Univ. Carlos III in Madrid {mehrdad,dunan}@gatech.edu manuelgr@mpi-sws.org {zha,lsong}@cc.gatech.edu ivalera@tsc.uc3m.es |
| Pseudocode | Yes | Algorithm 1: Average Instantaneous Intensity, Algorithm 2: PGD for Activity Shaping |
| Open Source Code | No | No explicit statement about releasing source code or a link to a repository was found. |
| Open Datasets | Yes | We use data gathered from Twitter as reported in [27], which comprises of all public tweets posted by 60,000 users during a 8-month period, from January 2009 to September 2009. |
| Dataset Splits | No | We first partition the 8-month data into 50 five-day long contiguous intervals. Then, we use one interval for training and the remaining 49 intervals for testing. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory) were mentioned for running the experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned for replicating the experiment. |
| Experiment Setup | Yes | We used a temporal resolution of one minute and selected the bandwidth ω = 0.1 by cross validation. |