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Journal Articles Computer Graphics Forum Year : 2023

ComBiNet: Visual Query and Comparison of Bipartite Multivariate Dynamic Social Networks

Abstract

We present ComBiNet, a visualization, query, and comparison system for exploring bipartite multivariate dynamic social networks. Historians and sociologists study social networks constructed from textual sources mentioning events related to people, such as marriage acts, birth certificates, and contracts. We model this type of data using bipartite multivariate dynamic networks to maintain a representation faithful to the original sources while not too complex. Relying on this data model, ComBiNet allows exploring networks using both visual and textual queries using the Cypher language, the two being synchronized to specify queries using the most suitable modality; simple queries are easy to express visually and can be refined textually when they become complex. These queries are used for applying topological and attribute-based selection on the network. Query results are visualized in the context of the whole network and over a geographical map for geolocalized entities. We also present the design of our interaction techniques for querying social networks to visually compare the selections in terms of topology, measures, and attribute distributions. We validate the query and comparison systems by showing how they have been used to answer historical questions and by explaining how they have been improved through a usability study conducted with historians.
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hal-03927450 , version 1 (06-01-2023)

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Alexis Pister, Christophe Prieur, Jean-Daniel Fekete. ComBiNet: Visual Query and Comparison of Bipartite Multivariate Dynamic Social Networks. Computer Graphics Forum, 2023, 42 (1), pp.290-304. ⟨10.1111/cgf.14731⟩. ⟨hal-03927450⟩
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