As opposed to traditional databases, graph databases store data as nodes and the relationships between them. This is a perfect fit for storing connect

Kenelyze’s January 2023 Release: Visualizing Data from Graph Databases

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2023-01-24 18:00:10

As opposed to traditional databases, graph databases store data as nodes and the relationships between them. This is a perfect fit for storing connected data (or, data shaped as a network), as it allows very quick lookups of network structures and enables flexible modeling of datasets as a set of interconnected entities from the outset.

Next to Kenelyze’s proven ability to generate all kinds of network visualizations from table-based datasets, it is now also possible to directly connect to Memgraph, Neo4j, AnzoGraph and ONgDB database instances and visualize any data of interest. The focus here is on allowing both non-technical and technical users to work with the data in the graph database: visualizations can be made based on searches and node expansions, while it is also possible to input custom Cypher queries and visualize more advanced data structures.

πŸ”Ž Search all node labels and properties in graph databases and visualize search results on the fly βž• Expand and collapse nodes and their neighbors to quickly populate subgraphs of interest πŸ’¬ Ingest users’ custom Cypher queries and visualize the resulting nodes and relationships directly ✏️ Edit the nodes, relationships and properties in the database directly from Kenelyze’s interface πŸ“Š Calculate graph and node-level metrics (e.g. community detection, node centrality measures) for any subgraph returned from the database πŸ“ Quickly generate network schemas to view the exact structure of the data model in the database πŸ“ Export any network created in Kenelyze (including those from local files) to Cypher queries to quickly populate graph databases with new data

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