Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes
Authors: Huawei Shen, Dashun Wang, Chaoming Song, Albert-László Barabási
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on a longitudinal citation dataset demonstrate that this model consistently outperforms existing popularity prediction methods. |
| Researcher Affiliation | Collaboration | Huawei Shen1 , Dashun Wang2, Chaoming Song3, Albert-L aszl o Barab asi4 1Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China 2IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, USA 3Department of Physics, University of Miami, Coral Gables, Florida 33146, USA 4Center for Complex Network Research, Northeastern University, Boston, Massachusetts 02115, USA |
| Pseudocode | No | The paper includes Figure 2, which is a graphical representation of the generative model, but it is not pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not include any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions using a dataset of 'all papers and citations published by American Physical Society between 1893 and 2009' and provides statistics in Table 1. However, it does not provide a specific link, DOI, repository name, or formal citation with authors/year for accessing this dataset. |
| Dataset Splits | No | The paper states: 'In principle, the values of prior parameters could be tuned by checking the accuracy of prediction function with respect to prior parameters on so-called validation set.' However, it does not explicitly describe a validation split (e.g., percentages or sample counts) used in their experiments. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not list any specific software or library names with version numbers that would be needed to replicate the experiment. |
| Experiment Setup | Yes | The training period is 10 years and we predict the citation counts for each paper from the 1st to 20th year after the training period. ... We set the parameter m = 30 for now... We set the threshold = 0.1 in this paper. |