A classification model based on a population of hypergraphs

TL;DR

This paper presents a new hypergraph classification algorithm that models multi-way interactions using a population of hypergraphs, showing improved performance over traditional methods on benchmark datasets.

Contribution

It introduces a hypergraph classification method that directly captures multi-way interactions and enhances robustness through a population of hypergraphs.

Findings

Demonstrates promising classification accuracy on two datasets.

Outperforms a generic random forest classifier.

Shows increased robustness with hypergraph populations.

Abstract

This paper introduces a novel hypergraph classification algorithm. The use of hypergraphs in this framework has been widely studied. In previous work, hypergraph models are typically constructed using distance or attribute based methods. That is, hyperedges are generated by connecting a set of samples which are within a certain distance or have a common attribute. These methods however, do not often focus on multi-way interactions directly. The algorithm provided in this paper looks to address this problem by constructing hypergraphs which explore multi-way interactions of any order. We also increase the performance and robustness of the algorithm by using a population of hypergraphs. The algorithm is evaluated on two datasets, demonstrating promising performance compared to a generic random forest classification algorithm.