RAIN: Social Role-Aware Information Diffusion
Authors: Yang Yang, Jie Tang, Cane Leung, Yizhou Sun, Qicong Chen, Juanzi Li, Qiang Yang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed model on a real social media data set. Our model performs much better in both microand macro-level prediction than several alternative methods. Experimental Results |
| Researcher Affiliation | Collaboration | Department of Computer Science and Technology, Tsinghua University, China Tsinghua National Laboratory for Information Science and Technology (TNList), China Huawei Noah s Ark Lab, Hong Kong College of Computer and Information Science, Northeastern University, USA |
| Pseudocode | No | The paper describes the model learning process using Gibbs sampling and provides mathematical equations, but it does not include a clearly labeled pseudocode block or algorithm. |
| Open Source Code | Yes | All data and codes used here are publicly available1. 1http://arnetminer.org/rain/ |
| Open Datasets | Yes | All data and codes used here are publicly available1. 1http://arnetminer.org/rain/ |
| Dataset Splits | Yes | We extracted the complete following relationships between users and all posting logs of November 1st, 2011 as the training set, and those of November 2nd, 2011 as the test set to evaluate the proposed model. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, memory, or cloud instance types) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions using LDA and Ranking SVM, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We empirically set the model parameters as: R = 10, α = 0.1, β = (1, 1), and γ = (1, 1). |