Goodness-of-Fit Tests for Inhomogeneous Random Graphs
Authors: Soham Dan, Bhaswar B. Bhattacharya
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare the performances of the different tests in simulations, and show that the proposed tests outperform the baseline tests across various natural random graphs models. |
| Researcher Affiliation | Academia | 1Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA, 2Department of Statistics, University of Pennsylvania, Philadelphia, USA. |
| Pseudocode | Yes | Algorithm 1 Bootstrapping a Test Statistic |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | No | The paper describes generating data from models like Erd os-R enyi and Planted Bisection for simulations, but does not refer to external publicly available datasets for training. |
| Dataset Splits | No | The paper uses the term 'validate' in the context of validating asymptotic results through simulation, not referring to a validation dataset split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments or simulations. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For our simulations, we fix the size of the graph n = 100, the sample size m = 4, and a reference edge-probability matrix Q(n) (which corresponds to the null), and consider samples G1, G2, G3, G4 i.i.d from IER(P (n)), where P (n) is a certain perturbation of the Q(n). The figures below show the empirical power of the tests over 1000 iterations (calibrated either using the asymptotic distribution or the parametric bootstrap at level α = 0.05) as the perturbation parameter increases. |