Hypergraph Modeling and Visualisation of Complex Co-occurence Networks

Ouvrard, Xavier; Goff, Jean‐Marie Le; Marchand-Maillet, Stéphane

doi:10.1016/j.endm.2018.11.011

Cited by 5 publications

(10 citation statements)

References 8 publications

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“…Recently, the introduction of more elaborate concepts such as multilayer networks (Boccaletti et al, 2014;Kivela et al, 2014;Zingg et al, 2020), hypergraphs (Ouvrard et al, 2017(Ouvrard et al, , 2018, and combinations thereof (Vasilyeva et al, 2021) supplements the view on complex networks (Boccaletti et al, 2006) and calls for new analysis methods (Fezzeh et al, 2021;Pisanski et al, 2020).…”

Section: Collaboration Networkmentioning

confidence: 99%

Application of graph theory in the library domain—Building a faceted framework based on a literature review

Lüschow

2021

Journal of Librarianship and Information Science

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Based on a literature review, we present a framework for structuring the application of graph theory in the library domain. Our goal is to provide both researchers and libraries with a standard tool to classify scientific work, at the same time allowing for the identification of previously underrepresented areas where future research might be productive. To achieve this, we compile graph theoretical approaches from the literature to consolidate the components of our framework on a solid basis. The extendable framework consists of multiple facets grouped into five categories whose elements can be arbitrarily combined. Libraries can benefit from these facets by using them as a point of reference for the (meta)data they offer. Further work on formally defining the framework’s categories as well as on integration of other graph-related research areas not discussed in this article (e.g. knowledge graphs) would be desirable and helpful in the future.

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Section: Collaboration Networkmentioning

confidence: 99%

Application of graph theory in the library domain—Building a faceted framework based on a literature review

Lüschow

2021

Journal of Librarianship and Information Science

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“…In a publication dataset, typical metadata types are: publication id, title, abstract, authors, affiliations, addresses, author keywords, publication categories, countries, organisations,... 3 There are many different navigation hyperedge possible: for instance choosing as reference the publication ids, the navigation hyperedge is: {authors, author keywords, organisations, country, publication categories}; choosing author keywords as reference the navigation hyperedge is: {authors, organisations, country, publication category, publication ids}.…”

Section: A Enhancing Navigationmentioning

confidence: 99%

“…Each element s of Σ ρ is mapped to a set of physical references R s = {r : s ∈ A ρ,r } ∈ P (S) in which they appear: we write r ρ the mapping. The multiset of values 3 Metadata of interest for visualisation or referencing are in italic The raw visualisation hb-graph for the facet of type α/ρ attached to the search S is then defined as:…”

Section: B Facet Visualisation Hb-graphsmentioning

confidence: 99%

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The HyperBagGraph DataEdron: An Enriched Browsing Experience of Datasets

Ouvrard

Goff

Marchand-Maillet

2020

SOFSEM 2020: Theory and Practice of Computer Science

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Traditional verbatim browsers give back information in a linear way according to a ranking performed by a search engine that may not be optimal for the surfer. The latter may need to assess the pertinence of the information retrieved, particularly when s·he wants to explore other facets of a multi-facetted information space. For instance, in a multimedia dataset different facets such as keywords, authors, publication category, organisations and figures can be of interest. The facet simultaneous visualisation can help to gain insights on the information retrieved and call for further searches. Facets are cooccurence networks, modeled by HyperBag-Graphs -families of multisets -and are in fact linked not only to the publication itself, but to any chosen reference. These references allow to navigate inside the dataset and perform visual queries. We explore here the case of scientific publications based on Arxiv searches.

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“…A multi-adic relationship can be viewed either as a group or a cooccurrence. Also, hypergraphs can be used to model the complex co-occurrence networks induced by the different facets of an information space- [25].…”

Section: Introductionmentioning

confidence: 99%

Exchange-based diffusion in Hb-Graphs

Ouvrard

Goff

Marchand-Maillet

2020

Multimed Tools Appl

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Highlighting important information of a network is commonly achieved by using random walks related to diffusion over such structures. Complex networks, where entities can have multiple relationships, call for a modeling based on hypergraphs. But, the limitation of hypergraphs to binary entities in co-occurrences has led us to introduce a new mathematical structure called hyperbaggraphs, that relies on multisets. This is not only a shift in the designation but a real change of mathematical structure, with a new underlying algebra. Diffusion processes commonly start with a stroke at one vertex and diffuse over the network. In the original conference article-(Ouvrard et al. 2018)-that this article extends we have proposed a two-phase step exchange-based diffusion scheme, in the continuum of spectral network analysis approaches, that takes into account the multiplicities of entities. This diffusion scheme allows to highlight information not only at the level of the vertices but also at the regrouping level. In this paper, we present new contributions: the proofs of conservation and convergence of the extracted sequences of the diffusion process, as well as the illustration of the speed of convergence and comparison between classical and modified random walks; the algorithms of the exchange-based diffusion and the modified random walk; the application to two use cases, one based on Arxiv publications and another based on Coco dataset images. All the figures have been revisited in this extended version to take the new developments into account.

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Hypergraph Modeling and Visualisation of Complex Co-occurence Networks

Cited by 5 publications

References 8 publications

Application of graph theory in the library domain—Building a faceted framework based on a literature review

Application of graph theory in the library domain—Building a faceted framework based on a literature review

The HyperBagGraph DataEdron: An Enriched Browsing Experience of Datasets

Exchange-based diffusion in Hb-Graphs

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