Hypergraph Modeling and Visualisation of Complex Co-occurence Networks

TL;DR

This paper presents a hypergraph-based framework for visualizing and navigating complex co-occurrence networks in datasets, enhancing knowledge discovery through multi-adic relationship modeling.

Contribution

It introduces a novel hypergraph modeling and visualization framework for co-occurrence networks, supporting efficient navigation across data facets.

Findings

Effective hypergraph visualization of co-occurrence networks

Enhanced navigation between data facets

Improved knowledge discovery capabilities

Abstract

Finding inherent or processed links within a dataset allows to discover potential knowledge. The main contribution of this article is to define a global framework that enables optimal knowledge discovery by visually rendering co-occurences (i.e. groups of linked data instances attached to a metadata reference) - either inherently present or processed - from a dataset as facets. Hypergraphs are well suited for modeling co-occurences since they support multi-adicity whereas graphs only support pairwise relationships. This article introduces an efficient navigation between different facets of an information space based on hypergraph modelisation and visualisation.