Reconstructing Diffusion Networks from Incomplete Data
Authors: Hao Huang, Keqi Han, Beicheng Xu, Ting Gan
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on both synthetic and real-world networks verify the effectiveness and efficiency of our approach. |
| Researcher Affiliation | Academia | Hao Huang , Keqi Han , Beicheng Xu and Ting Gan School of Computer Science, Wuhan University, China {haohuang, hankeqi, beichengxu, ganting}@whu.edu.cn |
| Pseudocode | No | The paper describes the steps of the LIDO algorithm in narrative text and equations, but it does not present them in a structured pseudocode block or algorithm box. |
| Open Source Code | No | The paper does not provide any statement about making its source code available or a link to a code repository. |
| Open Datasets | Yes | We adopt LFR benchmark graphs [Lancichinetti et al., 2008]...Net Sci [Newman, 2006], a co-authorship network...DUNF [Wang et al., 2014], a microblogging network...Meme Tracker (http://memetracker.org) |
| Dataset Splits | Yes | We simulate 200 times of diffusion processes on each network (β = 200), and randomly remove 15% of infection status observations as unobserved data (γ = 0.15), and use the remaining observation data Sobs for diffusion network reconstruction. |
| Hardware Specification | Yes | All algorithms in the experiments are implemented in Python, running on a desktop PC with Intel Core i3-6100 CPU at 3.70GHz and 8GB RAM. |
| Software Dependencies | No | The paper states that algorithms are "implemented in Python", but does not specify the version of Python or any other software libraries with their version numbers required for reproducibility. |
| Experiment Setup | Yes | In LIDO, the number m of sampling round is set to 6, the maximum number t of iterations is set to 5, and the stop condition for updating pji in each iteration is that the variation of each pji is less than 0.01. |