TGV: A Visualization Tool for Temporal Property Graph Databases

Diego Orlando (), Joaquín Ormachea (), Valeria Soliani () and Alejandro Ariel Vaisman ()
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Diego Orlando: Instituto Tecnológico de Buenos Aires Lavardén 315
Joaquín Ormachea: Instituto Tecnológico de Buenos Aires Lavardén 315
Valeria Soliani: Instituto Tecnológico de Buenos Aires and Hasselt University
Alejandro Ariel Vaisman: Instituto Tecnológico de Buenos Aires Lavardén 315

Information Systems Frontiers, 2024, vol. 26, issue 4, No 17, 1543-1564

Abstract: Abstract Graph databases are increasingly being used in the data science field, in particular to represent different kinds of networks. In real-world situations, the nodes and edges in a network evolve across time. For example, in a social network, people’s preferences and relationships change, as well as the characteristics of the network entities themselves. Temporal property graph databases aim at capturing these changes, by means of appropriate data models and query languages that allow users to represent, store, and query time-varying graphs. In order to exploit their full potential, temporal property graph databases require visualization tools that allow navigating graph data across time. To address this need, the present work introduces a framework for temporal property graph visualization, denoted TGV, based on T-GQL, a data model and query language for temporal graphs implemented over Neo4j, a widely-used graph database. TGV allows editing and running T-GQL queries, displaying the result, and navigating such result across time. Further, TGV displays temporal graphs in a transparent way, hiding the underlying T-GQL structure from the user.

Keywords: Graph visualization; Temporal graphs; Temporal database; Neo4j (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10796-023-10426-1

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