Online and Consistent Correlation Clustering
Authors: Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally we show experimentally that our algorithm achieves better performances than stateof-the-art algorithms on real world data. 5. Experiments |
| Researcher Affiliation | Collaboration | 1Google 2EPFL, Lausanne, Switzerland. |
| Pseudocode | Yes | Algorithm 1 AGREEMENT Algorithm 2 ONLINE AGREEMENT (Agree-On) |
| Open Source Code | Yes | Our code is written in C++ and is available online3. https://github.com/google-research/google-research/tree/master/online_correlation_clustering |
| Open Datasets | Yes | Our datasets are obtained from SNAP (Leskovec & Krevl, 2014); their basic characteristics are summarized in Table 1. |
| Dataset Splits | No | The paper describes an online setting where nodes arrive sequentially and does not specify traditional train/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | Yes | We run our experiments on a e2-standard-16 Google Cloud instance, with 16 cores, 2.20GHz Intel(R) Xeon(R) processor, and 64 Gi B main memory. |
| Software Dependencies | No | The paper states, 'Our code is written in C++,' but does not provide specific version numbers for any libraries, frameworks, or compilers used. |
| Experiment Setup | Yes | In our experiments we set the parameters β = λ = 0.2, as this setting exhibited the best behavior in (Cohen-Addad et al., 2021). That is, whenever a new node arrives it is inserted in the preexisting random order at a random position (the relative order of the previously arrived nodes remains unchanged). |