# The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets

## TL;DR

This paper introduces HyperBagGraph-based visual browsing for multimedia datasets, enabling multi-faceted exploration and insights beyond linear search results, demonstrated on scientific publications from Arxiv.

## Contribution

It proposes HyperBagGraphs for multi-facet visualization and navigation in multimedia datasets, enhancing exploratory search capabilities.

## Key findings

- Facets are modeled as HyperBag-Graphs for co-occurrence networks.
- Faceted visualization improves exploration of multimedia datasets.
- Applied to Arxiv publications for scientific data browsing.

## Abstract

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$\cdot$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 co-occurence 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.