Graph Partitioning with a Move Budget
Abstract
In many real world networks, there already exists a (not necessarily optimal) $k$-partitioning of the network. Oftentimes, for such networks, one aims to find a $k$-partitioning with a smaller cut value by moving only a few nodes across partitions. The number of nodes that can be moved across partitions is often a constraint forced by budgetary limitations. Motivated by such real-world applications, we introduce and study the $r$-move $k$-partitioning problem, a natural variant of the Multiway cut problem. Given a graph, a set of $k$ terminals and an initial partitioning of the graph, the $r$-move $k$-partitioning problem aims to find a $k$-partitioning with the minimum-weighted cut among all the $k$-partitionings that can be obtained by moving at most $r$ non-terminal nodes to partitions different from their initial ones. Our main result is a polynomial time $3(r+1)$ approximation algorithm for this problem. We further show that this problem is $W[1]$-hard, and give an FPTAS for when $r$ is a small constant.
Cite
Text
Dalirrooyfard et al. "Graph Partitioning with a Move Budget." Artificial Intelligence and Statistics, 2024.Markdown
[Dalirrooyfard et al. "Graph Partitioning with a Move Budget." Artificial Intelligence and Statistics, 2024.](https://mlanthology.org/aistats/2024/dalirrooyfard2024aistats-graph/)BibTeX
@inproceedings{dalirrooyfard2024aistats-graph,
title = {{Graph Partitioning with a Move Budget}},
author = {Dalirrooyfard, Mina and Fata, Elaheh and Behbahani, Majid and Nevmyvaka, Yuriy},
booktitle = {Artificial Intelligence and Statistics},
year = {2024},
pages = {568-576},
volume = {238},
url = {https://mlanthology.org/aistats/2024/dalirrooyfard2024aistats-graph/}
}