DynaDiffuse: A Dynamic Diffusion Model for Continuous Time Constrained Influence Maximization
Authors: Miao Xie, Qiusong Yang, Qing Wang, Gao Cong, Gerard Melo
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real social network data show that our model finds higher quality solutions and our algorithm outperforms state-of-art alternatives. |
| Researcher Affiliation | Collaboration | 1University of Chinese Academy of Sciences, China, {xiemiao, qiusong, wq} @nfs.iscas.cn 2National Engineering Research Center of Fundamental Software, Institute of Software, Chinese Academy of Sciences 3State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of China. 4School of Computer Engineering, Nanyang Technological University, Singapore, gaocong@ntu.edu.sg 5Tsinghua University/Microsoft Research Asia, China, gdm@demelo.org |
| Pseudocode | Yes | Algorithm 1 Complete Modeling Process |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper uses a "microblog diffusion dataset from Sina Weibo", but no concrete access information (such as a link, DOI, or a specific citation for public availability) is provided for this dataset. |
| Dataset Splits | No | The paper mentions evaluating on a Sina Weibo dataset but does not specify how the data was split into training, validation, or test sets with percentages or sample counts. |
| Hardware Specification | Yes | Figure 4 shows running time on a workstation (2.5GHz Dual core, 4GB RAM). |
| Software Dependencies | No | The paper mentions using the "PRISM system (Kwiatkowska, Norman, and Parker 2011)" but does not specify its version number or other software dependencies with specific versions. |
| Experiment Setup | Yes | Herd behavior module: δ = 30, b = 0.05, activeness module: decrease Delta = 0.9, decrease Rate = 0.2. |