GraphML for Neo4j

100%

GraphML for Neo4j

✓ Published0🌍 Public

Last edited Apr 2, 2017

Created on Apr 2, 2017

This graph visualization maps academic works, authors, and categories from a theological library dataset, revealing relationships between publications and their contributors. The graph connects nodes representing books, authors, and subject categories, using directional edges to show authorship and classification. The implementation parses GraphML-formatted XML data and displays it through the Neo4j graph platform, leveraging the force-directed layout to position nodes and illustrate the network's structure. The visualization highlights recurring patterns, such as multiple authors associated with the same work and works appearing in several categories.

AI-generated description

Similar vizzes

Loading thumbnail…

GraphML data

This example visualizes a directed graph of library catalog records, showing nodes for authors, works, and subject categories connected by edges. The graph uses GraphML data parsed from an XML file, with node labels and properties like author names and categories displayed. The visualization is rendered using D3.js force-directed layout, where nodes are positioned by a physics simulation and edges show relationships. The data comes from a gist by CliffordAnderson, illustrating a bibliographic network.

CCliffordAnderson

81% match

Loading thumbnail…

XQuery for Author-Work-Category Graphs

An XQuery script transforms an ATLA RDB RSS feed into a GraphML network graph, linking author, work, and category nodes via hashed IDs and directed edges. The resulting graph is loaded into Neo4j using the APOC `apoc.import.graphml` procedure, which reads labels and relationships directly from the GraphML file. The visualization, rendered in Neo4j, shows the interconnected scholarly relationships, with authors and categories clustered around their respective works.

CCliffordAnderson

78% match

Loading thumbnail…

Gist a55b95a43d04067df4c11d576bed3c42

This example demonstrates a graph database model for scholarly communications in theology, showing relationships between academic institutions, faculty, and the American Theological Library Association (ATLA). Built with Neo4j's Cypher query language, the visualization renders the schema and sample data as an interactive network graph. Using Neo4j's built-in graph visualization features, the code displays nodes for institutions like Princeton Theological Seminary and Drew University, connected by membership edges to the ATLA hub, while also including a faculty node for Clifford B. Anderson.

CCliffordAnderson

77% match

Loading thumbnail…

GraphML data for Andy Keck

This example visualizes a bipartite GraphML network connecting twelve authors to their associated subject categories, such as “Cataloging” and “Electronic books.” The graph encodes relationships through directed edges, with author names and category labels displayed as node attributes. The visualization renders the data using the GraphML parsing capabilities of a graph library, likely with D3.js’s force-directed layout to position nodes and display the interconnected structure of the author-topic graph.

CCliffordAnderson

75% match

Loading thumbnail…

Clifford Anderson's Scholarly Graph from Atlas

This visualization displays Clifford Anderson’s scholarly network as a directed graph, mapping author relationships and their multifaceted roles (editor, translator, introducer) through distinct node labels and edges. The graph, sourced from a GraphML file, uses the `d3.forceSimulation` API to position nodes via a physics-based layout, while edge and node elements are rendered through SVG paths and circles, with labels encoded in the XML data. The visualization reveals collaboration clusters and role-based connections among authors like Kuyper and Harinck, emphasizing the structured, relational nature of academic publishing.

CCliffordAnderson

70% match

Loading thumbnail…

Graph Gist

This example demonstrates a simple graph visualization of two people and their database preferences. It shows nodes for "You" and "They," each linked via a `LIKE` relationship to database nodes representing Neo4j and eXistDB. The data comes from a Cypher query that creates the graph structure, and the visualization renders it using the D3.js library with a force-directed layout. Specifically, `d3.forceSimulation` drives node positioning, while SVG elements for circles and lines represent entities and relationships. The graph is rendered as an interactive network, highlighting the connections between the person nodes and their chosen database technologies.

CCliffordAnderson

70% match

Loading thumbnail…

Graph Analysis for the Humanities

This workshop introduces humanities scholars to graph-based data modeling and analysis, presenting a series of hands-on exercises that build from introductory Neo4j concepts to practical applications. It demonstrates how to create graph data models, write Cypher queries, and render interactive visualizations in the browser. The material shows loading Berlin metro data from Wikidata via XQuery and BaseX, then visualizing those connections with Neovis.js, a JavaScript library built on Neo4j's Bolt protocol. The exercise set culminates in web development, using a Python SimpleHTTPServer to deploy graph-based websites.

CCliffordAnderson

68% match

Loading thumbnail…

Smiley's Graph in the ATLA Religion Index

This example shows a small directed graph from the ATLA Religion Index, revealing relationships among an author, two scholarly works, and a peer-reviewed category. Three node types—author, category, and work—are connected by labeled `ABOUT` and `CREATED` edges. The visualization is constructed from a GraphML file using the GraphML XML schema, with graph data parsed to define nodes and edges. The layout is not specified in the source data, so the graph appears as a static schematic. The implementation relies on GraphML parsing libraries to interpret the XML structure, though no specific rendering library is visible in the provided code.

CCliffordAnderson

68% match