Learning Diffusions without Timestamps
Authors: Hao Huang, Qian Yan, Ting Gan, Di Niu, Wei Lu, Yunjun Gao582-589
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both synthetic and real-world networks are conducted, and the results verify the effectiveness and efficiency of our approach. |
| Researcher Affiliation | Academia | 1School of Computer Science, Wuhan University, China 2Department of Electrical and Computer Engineering, University of Alberta, Canada 3School of Information and DEKE, MOE, Renmin University of China, China 4College of Computer Science and Technology, Zhejiang University, China |
| Pseudocode | Yes | Algorithm 1: The TWIND Algorithm |
| Open Source Code | No | The paper does not provide explicit statements or links for open-source code availability. |
| Open Datasets | Yes | We adopt LFR benchmark graphs (Lancichinetti, Fortunato, and Radicchi 2008) as the synthetic networks. ... In addition, we adopt two real-world networks, i.e., Net Sci (Newman 2006) ... and DUNF (Wang et al. 2014) |
| Dataset Splits | No | The paper describes how infection data is generated on the networks, but it does not specify explicit training, validation, or test dataset splits for reproducibility of pre-existing datasets. |
| Hardware Specification | Yes | All algorithms in the experiments are implemented in Java, running on a desktop PC with Intel Core i3-6100 CPU at 3.70GHz and 8GB RAM. |
| Software Dependencies | No | The paper mentions 'implemented in Java' but does not specify a Java version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | In each simulation, 0.15n nodes are randomly selected as the initial infected nodes (i.e., α = 0.15). ... In each diffusion process, each infected node tries to infect its uninfected child nodes with a transmission rate, which subjects to a Gaussian distribution with a mean of 0.3 and a standard deviation of 0.05, to make about 95% of transmission rate values are within a range from 0.2 to 0.4. |