2018/09/01 by Xavier Ouvrard, Ouvrard, Xavier, Jean‐Marie Le Goff +3
Computer Science · Physics and Astronomy · #Combinatorics (math.CO) #Complex Network Analysis Techniques #Data Management and Algorithms #Data Visualization and Analytics #Databases (cs.DB) #Discrete Mathematics (cs.DM) #FOS: Computer and information sciences #FOS: Mathematics #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1809.00164
openalex publication_date 2018/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.