Spectral vertex sparsifiers and pair-wise spanners over distributed graphs
Authors: Chunjiang Zhu, Qinqing Liu, Jinbo Bi
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |