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..
Random Intersection Graphs and Missing Data
Authors: Dror Salti, Yakir Berchenko5579-5585
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide two examples corresponding to these threshold phenomena and illustrate the theoretical predictions with simulations that are consistent with our reduction. |
| Researcher Affiliation | Academia | Dror Salti, Yakir Berchenko Dept. of Industrial Engineering and Management Ben-Gurion University of the Negev Beersheba, Israel EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific links or explicit statements of code release) for the source code. |
| Open Datasets | No | The data were simulated in the form of a linear regression model: yi = xi, β + εi, i = 1, 2, . . . , n. The paper describes how the data was simulated, rather than referring to a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes generating data for simulations and applying the EM algorithm, but it does not specify explicit training, validation, or test dataset splits in the traditional sense. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory used for running its experiments. |
| Software Dependencies | No | The paper mentions using "the norm package in the R program" but does not specify version numbers for R or the `norm` package, which is required for reproducibility. |
| Experiment Setup | Yes | In Figure 3, we plot the results of simulations using n = 100, m = 30, X N(0, I), εi N(0, 1) i. We used five different probabilities p {0.02, 0.022, 0.024, 0.026, 0.028} and n = 100. |