General Centrality in a hypergraph

TL;DR

This paper introduces a new centrality measure for hypergraph nodes, extending eigenvector centrality and general centrality concepts to better understand communication, learning, and network construction in hypergraphs.

Contribution

It proposes a novel centrality measurement for hypergraphs based on existing eigenvector and general centrality theories, enabling advanced analysis of hypergraph networks.

Findings

Provides a new centrality measure for hypergraphs

Enables analysis of communication in hypergraph networks

Supports implementation of learning mechanisms in hypergraph structures

Abstract

The goal of this paper is to present a centrality measurement for the nodes of a hypergraph, by using existing literature which extends eigenvector centrality from a graph to a hypergraph, and literature which give a general centrality measurement for a graph. We will use this measurement to say more about the number of communications in a hypergraph, to implement a learning mechanism, and to construct certain networks.