Diffusion Source Identification on Networks with Statistical Confidence
Authors: Quinlan E Dawkins, Tianxi Li, Haifeng Xu
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |