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..
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models
Authors: Aditya Gangrade, Praveen Venkatesh, Bobak Nazer, Venkatesh Saligrama
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate these phenomena numerically on SBMs and on real-world datasets as well as Markov Random Fields where we only observe node data rather than the existence of links. We complement the above theoretical study by three experiments: the first implements the above tests on synthetic SBMs, and the second on the political blogs dataset a popular real world dataset for community detection [AG05]. Both of these experiments show excellent agreement with the theoretical predictions. |
| Researcher Affiliation | Academia | Aditya Gangrade Boston University EMAIL Praveen Venkatesh Carnegie Mellon University EMAIL Bobak Nazer Boston University EMAIL Venkatesh Saligrama Boston University EMAIL |
| Pseudocode | Yes | Algorithm 1: Two Sample Tester(G, H, δ) |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide links to a code repository for the methodology described. |
| Open Datasets | Yes | Next, we demonstrate that our tests perform similarly for a real dataset, specifically the Political Blogs dataset [AG05]. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., exact percentages or sample counts). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper mentions using Python libraries such as SciPy, NumPy, Matplotlib, and Scikit-learn, but it does not specify their version numbers. |
| Experiment Setup | Yes | Recovery is performed by regularised spectral clustering, for which a detailed description is given in Appendix C.1. In Appendix C.1, it states: 'Spectral clustering requires a choice of k. In all experiments we set k = 2. It also requires a normalisation for the adjacency matrix. For the SBM experiments, we use the normalised adjacency matrix A = D−1/2AD−1/2. The regularised spectral clustering for sparse graphs is described in [JY16]. It involves adding µnI to the adjacency matrix prior to computing the eigenvalues. We set µn = p log n/(n(a + b)).' |