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..
Spectral vertex sparsifiers and pair-wise spanners over distributed graphs
Authors: Chunjiang Zhu, Qinqing Liu, Jinbo Bi
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are performed to validate the communication efficiency of the proposed algorithms under the guarantee that the constructed sparsifiers have a good approximation quality. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of North Carolina at Greensboro, Greensboro, NC, USA 2Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA. |
| Pseudocode | Yes | Algorithm 1 Local SC |
| Open Source Code | No | For the spectral sparsification, we employ the implementation of Spielman and Srivastava (Spielman & Srivastava, 2011) 3. github.com/danspielman/Laplacians.jl. This refers to a third-party implementation, not the authors' own source code for their proposed methods. |
| Open Datasets | No | We use two synthetic datasets, Circles and Gaussians, and four real-world datasets Sculpture, Sculpture-1M, Sculpture-11M, and Beach. The paper mentions these datasets but does not provide specific links, DOIs, repositories, or formal citations with authors and years for public access. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., exact percentages or sample counts for training, validation, and test sets) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | all experiments were performed in a machine with Intel i7-9750H 2.6GHz CPU and 16G RAM. |
| Software Dependencies | No | The algorithms were implemented using Matlab and Julia programs. The paper mentions these software environments but does not provide specific version numbers for them or any libraries used. |
| Experiment Setup | Yes | In the baseline setting, the number of sites s = 5, the sampling rate r = 0.05, (i.e., |T| = 0.05n) and the approximation parameter ϵ = 0.3. |