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TGV: A Visualization Tool for Temporal Property Graph Databases
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AI
DOI：10.1007/s10796-023-10426-1.png)
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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.
Keyword:
Graph visualization
Temporal graphs
Temporal database
Neo4j
期刊
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8.3
论文数:
2.0K
被引数:
6.5K
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