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..
Dynamic Correlation Clustering in Sublinear Update Time
Authors: Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally we complement our theoretical analysis with experiments on real world data. and We conduct two sets of experiments. |
| Researcher Affiliation | Collaboration | 1Google Research 2Columbia University. |
| Pseudocode | Yes | Algorithm 1 AGREEMENTALGORITHM(G), Algorithm 2 Notify(u, ϵ), Algorithm 3 Dynamic Agreement (DA), Algorithm 4 Connect(u, ϵ, t), Algorithm 5 Anchor(u, ϵ, t), Algorithm 6 Clean(u, ϵ, t), Algorithm 7 Compute Connected Components( e G), Algorithm 8 PROBABILISTICAGREEMENT(u, v, ϵ), Algorithm 9 HEAVY(u, ϵ) |
| Open Source Code | Yes | Our code is written in Python 3.11.5 and is available at https://github.com/andreasr27/DCC. |
| Open Datasets | Yes | We use four graphs from SNAP (Leskovec & Krevl, 2014)... and we use Drift (Vergara et al., 2012; Rodriguez Lujan et al., 2014) from the UCI Machine Learning Repository (Dua & Graff, 2017). |
| Dataset Splits | No | The paper describes how dynamic node streams are created ('we first create a random arrival sequence for all the nodes. Subsequently in between any two additions, with probability p, we select at random a node of the current graph and delete it.'), but does not specify train/validation/test splits as the task is dynamic clustering on evolving graphs. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory used for running experiments. |
| Software Dependencies | Yes | Our code is written in Python 3.11.5 |
| Experiment Setup | Yes | We set the deletion probability in between any two node arrivals to be 0.2. The agreement parameter is set to ϵ = 0.2... all our subroutines use a random sample of size 2 and the probability of a node joining the anchor set is set to 20/du... |