Efficient Graph Matching for Correlated Stochastic Block Models
Authors: Shuwen Chai, Miklos Z. Racz
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 shuwenchai2027@u.northwestern.edu Miklós Z. Rácz Northwestern University Evanston, IL 60208 miklos.racz@northwestern.edu |
| 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. |