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.
@article{arxiv.1809.00164,
title = {Hypergraph Modeling and Visualisation of Complex Co-occurence Networks},
author = {Xavier Ouvrard and Jean-Marie Le Goff and Stephane Marchand-Maillet},
journal= {arXiv preprint arXiv:1809.00164},
year = {2018}
}
Comments
Preprint submitted at ENDM Special Journal 2nd IMA Conference on Theoretical and Computational Discrete Mathematics