Communication-Optimal Distributed Dynamic Graph Clustering
Authors: Chun Jiang Zhu, Tan Zhu, Kam-Yiu Lam, Song Han, Jinbo Bi5957-5964
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted extensive experiments on both synthetic and real-life datasets which confirmed the communication efficiency of our approach over baseline algorithms while achieving comparable clustering results. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA {chunjiang.zhu, tan.zhu, song.han, jinbo.bi}@uconn.edu 2Department of Computer Science, City University of Hong Kong, Hong Kong, PRC cskylam@cityu.edu.hk |
| Pseudocode | Yes | Algorithm 1: D2-CABL at Time Point τ |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | No | The paper describes the 'Gaussians dataset' and 'Sculpture dataset' used for experiments, but it does not provide concrete access information (e.g., a direct link, DOI, or formal citation for public availability) for these datasets. Footnotes for other data sources are motivational examples, not the experimental datasets. |
| Dataset Splits | No | The paper does not specify exact train/validation/test split percentages, absolute sample counts for splits, or reference predefined splits with citations for reproducibility. |
| Hardware Specification | Yes | We implemented all five algorithms in Matlab programs, and conducted the experiments on a machine equipped with Intel i7 7700 2.8GHz CPU, 8G RAM and 1T disk storage. |
| Software Dependencies | No | The paper states, 'We implemented all five algorithms in Matlab programs,' but it does not provide specific version numbers for Matlab or any other software libraries or dependencies used. |
| Experiment Setup | Yes | As the baseline setting, we selected the total number of time points t = 10 and the total number of sites s = 30. We randomly chose 5% of edges to delete at a random time point after their arrival. |