Random Intersection Graphs and Missing Data
Authors: Dror Salti, Yakir Berchenko5579-5585
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 saltidr@post.bgu.ac.il, berchenk@bgu.ac.il |
| 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. |