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- W2891323656 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." @default.
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- W2891323656 date "2018-12-01" @default.
- W2891323656 modified "2023-09-23" @default.
- W2891323656 title "Hypergraph Modeling and Visualisation of Complex Co-occurence Networks" @default.
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- W2891323656 doi "https://doi.org/10.1016/j.endm.2018.11.011" @default.
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