On the Theoretical Properties of the Network Jackknife
Authors: Qiaohui Lin, Robert Lunde, Purnamrita Sarkar
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical analysis with a range of simulated and real-data examples and show that the network jackknife offers competitive performance in cases where other resampling methods are known to be valid. |
| Researcher Affiliation | Academia | 1Department of Statistics and Data Sciences, University of Texas at Austin, TX, USA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | In the second experiment, we look at three college pairs: Berkeley and Stanford, Yale and Princeton, Harvard and MIT. First we decide which statistic differentiates between a given pair. For this, we split each college data set in half, into a training set and test set... We use Facebook network data (Rossi & Ahmed, 2015). |
| Dataset Splits | No | The paper mentions splitting data into "training set" and "test set" but does not explicitly mention a "validation" split. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | From these two graphons, we consider graph size n of n = 100, 500, 1000, 2000, 3000. For each n, we simulated 100 graphs to calculate the approximate true variance of edge density, triangle density, two-star density and normalized transitivity among these graphs. ... For this, we split each college data set in half, into a training set and test set. |