Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Reconstructing Diffusion Networks from Incomplete Data
Authors: Hao Huang, Keqi Han, Beicheng Xu, Ting Gan
IJCAI 2022 | Venue PDF | 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 EMAIL |
| 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. |