Learning User-Specific Latent Influence and Susceptibility from Information Cascades
Authors: Yongqing Wang, Huawei Shen, Shenghua Liu, Xueqi Cheng
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic dataset and a large-scale microblogging dataset demonstrate that this model outperforms the existing pair-wise models at predicting cascade dynamics, cascade size, and who will be retweeted . |
| Researcher Affiliation | Academia | Yongqing Wang , Huawei Shen , Shenghua Liu and Xueqi Cheng CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China wangyongqing@software.ict.ac.cn, {shenhuawei,liushenghua,cxq}@ict.ac.cn |
| Pseudocode | Yes | Algorithm 1 Parameter estimation |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the methodology described. |
| Open Datasets | Yes | The Microblog data from Sina Weibo website is published by WISE 2012 Challenge1, spanning from January 1, 2011 to Feburary 15, 2011. http://www.wise2012.cs.ucy.ac.cy/challenge.html |
| Dataset Splits | No | The paper specifies training and testing splits (e.g., 80% for training on synthetic data, and temporal splits D1-D3 for training and T1-T3 for testing on microblog data), but does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set the dimensionality of user s influence Iu and susceptibility Su as 5... with λ = 0.01. ... We introduce the length of cascade context l... The parameters of the model are learned by minimizing the negative logarithmic likelihood... using Projected Gradient (PG) method... Input: Collection of cascades observed in a given time period, the maximum epoch M, and regularization parameters γI and γS |