Correlation Clustering in Constant Many Parallel Rounds
Authors: Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our analysis with an experimental analysis of our techniques. ... We complement our theoretical results with an empirical analysis showing that our MPC algorithm is significantly faster than previously known algorithms (Chierichetti et al., 2014; Pan et al., 2015). Furthermore, despite its theoretical approximation guarantees being inferior to previous work, in our experiments the quality of the solution is better. ... In Section 4 we present our experimental results. |
| Researcher Affiliation | Collaboration | 1Google Research, Z urich, Switzerland 2CSAIL, MIT, Cambridge, MA, USA 3Microsoft Research, Redmond, USA. |
| Pseudocode | Yes | Algorithm 1 Correlation-Clustering(G) ... Algorithm 2 Agreement(u, v) ... Algorithm 3 Connected-Components |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that the code for their methodology is publicly available. |
| Open Datasets | Yes | All our datasets were obtained from The Laboratory for Web Algorithmics4 (Boldi & Vigna, 2004; Boldi et al., 2011; 2004), and some of their statistics are summarized in Table 1. The dblp-2011 dataset is the DBLP co-authorship network from 2011, uk-2005 is a 2005 crawl of the .uk domain, it-2004 a 2004 crawl of the .it domain, twitter-2010 a 2010 crawl of twitter, and webbase-2001 is a 2001 crawl by the Web Base crawler. ... 4http://law.di.unimi.it/datasets.php |
| Dataset Splits | No | The paper mentions the datasets used for evaluation but does not provide specific details on training, validation, or test splits. It uses existing graphs for its empirical analysis. |
| Hardware Specification | No | The paper states 'We used 10 machines across all experiments', but it does not specify any particular hardware details such as CPU models, GPU models, or memory specifications. |
| Software Dependencies | No | The paper describes algorithms and experiments but does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | Our algorithm has two parameters λ, β which affect the approximation of the algorithm... For simplicity, we set λ = β {0.05, 0.1, 0.2}. To refer to an algorithm with a specific parameter, we append the parameter value to the algorithm name, e.g., we say OURALGO-0.05. ... We adopt the setting of ϵ from Pan et al. (2015), and use ϵ {0.1, 0.5, 0.9} for both algorithms. |