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..
Diffusion Source Identification on Networks with Statistical Confidence
Authors: Quinlan E Dawkins, Tianxi Li, Haifeng Xu
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our approach via extensive synthetic experiments on well-known random network models, a large data set of hundreds of real-world networks, as well as a mobility network between cities concerning the COVID-19 spreading. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Virginia, Charlottesville, Virginia, USA 2Department of Statistics, University of Virginia, Charlottesville, Virginia, USA. |
| Pseudocode | Yes | Algorithm 1 Vanilla MC for Confidence Set Construction |
| Open Source Code | Yes | All source code of this paper can be found in hyperlink https://github.com/labsigma/Diffusion-Source-Identification. |
| Open Datasets | Yes | We generate networks from three random network models: random 4-regular trees, the preferential attachment model (Barab asi & Albert, 1999) and the small-world (S-W) network model (Watts & Strogatz, 1998). |
| Dataset Splits | No | The paper evaluates the coverage rate of its confidence sets and uses Monte Carlo simulations but does not provide specific details on train/validation/test dataset splits needed for model reproduction in a machine learning context. |
| Hardware Specification | No | The paper mentions running experiments 'on 20 cores' but does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names, frameworks with versions) are mentioned. |
| Experiment Setup | Yes | The Monte Carlo size m is 10000. |