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 Graph Matching for Correlated Stochastic Block Models
Authors: Shuwen Chai, Miklos Z. Racz
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result gives the first efficient algorithm for graph matching in this setting. Our main contribution is the theoretical analysis of a novel efficient graph matching algorithm for correlated SBMs. |
| Researcher Affiliation | Academia | Shuwen Chai Northwestern University Evanston, IL 60208 EMAIL Miklós Z. Rácz Northwestern University Evanston, IL 60208 EMAIL |
| Pseudocode | Yes | Algorithm 1 Almost Exact Graph Matching for CSBM, Algorithm 2 Seeded Graph Matching, Algorithm 3 Almost-exact Community Recovery, Algorithm 4 Efficient Almost Exact Graph Matching Algorithm |
| Open Source Code | No | The paper states it does not include experimental results and does not provide any explicit statements about releasing source code for the methodology or links to a code repository. |
| Open Datasets | No | This is a theoretical paper focused on algorithm design and proofs for correlated stochastic block models. It does not conduct empirical studies with specific named datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not include empirical experiments, thus there is no mention of validation dataset splits. |
| Hardware Specification | No | This paper is theoretical and does not report on experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not report on experiments, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | This is a theoretical paper and does not include details about an experimental setup, hyperparameters, or system-level training settings. |