Hypergraph Modeling and Visualisation of Complex Co-occurence Networks

Q2 Mathematics

Electronic Notes in Discrete Mathematics Pub Date : 2018-12-01 DOI:10.1016/j.endm.2018.11.011

X. Ouvrard , J.M. Le Goff , S. Marchand-Maillet

引用次数: 5

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.

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复杂共现网络的超图建模与可视化

在数据集中查找固有的或经过处理的链接可以发现潜在的知识。本文的主要贡献是定义了一个全局框架，通过可视化地呈现来自数据集的共现(即附加到元数据引用的链接数据实例组)——无论是固有的还是经过处理的——来实现最佳的知识发现。超图非常适合于共现建模，因为它们支持多重性，而图只支持成对关系。本文介绍了基于超图建模和可视化的信息空间的不同方面之间的有效导航。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Electronic Notes in Discrete Mathematics Mathematics-Discrete Mathematics and Combinatorics

CiteScore

1.30

自引率

0.00%

发文量

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