# Correlation Clustering Algorithm for Dynamic Complete Signed Graphs: An Index-based Approach

arXiv: https://arxiv.org/abs/2301.00384 · HTML: https://mlsanity.com/paper/2301.00384/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 1 Jan 2023.

## Builds on (most cited first, 3 of 3)

- [Multi-scale Attributed Node Embedding](https://mlsanity.com/paper/1909.13021/) (2019)
- [Correlation Clustering in Constant Many Parallel Rounds](https://mlsanity.com/paper/2106.08448/) (2021)
- [Online Correlation Clustering for Dynamic Complete Signed Graphs](https://mlsanity.com/paper/2211.07000/) (2022)

## Built on by (most cited first, 1 of 1)

- [Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in eDiscovery](https://mlsanity.com/paper/2405.19164/) (2024)

## Abstract (arXiv)

In this paper, we reduce the complexity of approximating the correlation clustering problem from O(m×left( 2+ α (G) right)+n) to O(m+n) for any given value of varepsilon for a complete signed graph with n vertices and m positive edges where α(G) is the arboricity of the graph. Our approach gives the same output as the original algorithm and makes it possible to implement the algorithm in a full dynamic setting where edge sign flipping and vertex addition/removal are allowed. Constructing this index costs O(m) memory and O(m×α(G)) time. We also studied the structural properties of the non-agreement measure used in the approximation algorithm. The theoretical results are accompanied by a full set of experiments concerning seven real-world graphs. These results shows superiority of our index-based algorithm to the non-index one by a decrease of %34 in time on average.