An Improved Combinatorial Algorithm for Edge-Colored Clustering in Hypergraphs

TL;DR

This paper introduces a novel combinatorial approximation algorithm for the NP-hard edge-colored clustering problem in hypergraphs, achieving a better approximation factor than previous methods, thus improving scalability and effectiveness.

Contribution

It presents the first combinatorial approximation algorithm with an approximation factor better than 2 for edge-colored hypergraph clustering.

Findings

Achieves an approximation factor better than 2

First combinatorial algorithm with improved approximation ratio

Enhances scalability for clustering in complex hypergraphs

Abstract

Many complex systems and datasets are characterized by multiway interactions of different categories, and can be modeled as edge-colored hypergraphs. We focus on clustering such datasets using the NP-hard edge-colored clustering problem, where the goal is to assign colors to nodes in such a way that node colors tend to match edge colors. A key focus in prior work has been to develop approximation algorithms for the problem that are combinatorial and easier to scale. In this paper, we present the first combinatorial approximation algorithm with an approximation factor better than 2.