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..
Improved Communication Cost in Distributed PageRank Computation – A Theoretical Study
Authors: Siqiang Luo
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Improved Communication Cost in Distributed Page Rank Computation A Theoretical Study |
| Researcher Affiliation | Academia | 1Harvard University. Correspondence to: Siqiang Luo <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Estimating Page Ranks based on Unit Task; Algorithm 2 Estimating Page Ranks based on Simple Unit Task; Algorithm 3 Estimating Page Ranks with Improved Bandwidth |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that open-source code for the described methodology is available. |
| Open Datasets | No | The paper is a theoretical study and does not describe experiments using specific datasets, nor does it provide information about dataset availability. It refers to a 'graph of n nodes' as a theoretical construct. |
| Dataset Splits | No | The paper is a theoretical study and does not mention dataset splits for training, validation, or testing, as it does not conduct empirical experiments. |
| Hardware Specification | No | The paper is a theoretical study and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is a theoretical study and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is a theoretical study and does not include details about an experimental setup, such as hyperparameters or system-level training settings, as it does not conduct empirical experiments. |