A learning exercise for Neo4j

Graph Gist

This example shows a small graph of people and their database preferences, capturing two `Person` nodes connected by `LIKE` relationships to database nodes labeled `Database`, `NoSql`, and `Graph`. The visualization renders this graph data using D3.js, likely with a force-directed layout (`d3.forceSimulation`) to position nodes and links, and possibly `d3.geoPath` or standard SVG/CSS for shapes and labels. The source code, authored by CliffordAnderson and stored as a Gist, explicitly creates the nodes and relationships with Cypher, returning them for immediate display. The result emphasizes how graph structures and their labels can be directly mapped from query output to interactive visual elements.

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.

Graph Gist

This example renders a small Cypher query result as an interactive force-directed graph. It uses d3.forceSimulation to position nodes and links, displaying person and database entities with their LIKE relationships from the Neo4j sample data. The graph draws SVG circles and connecting lines, with labels for each node, and updates positions through the simulation's tick events.

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.

Neo4j REST example

This example demonstrates a graph visualization of a Neo4j query result, showing how a single person node (e.g., “Stanley Kubrick”) is returned and rendered as a node in a force-directed layout. The code issues a Cypher POST request to the Neo4j REST endpoint, then uses d3.forceSimulation to position the node and draw it with SVG circles and labels. The visualization focuses on the raw response data, displaying only the returned `a` object without additional graph traversal or styling beyond basic node rendering.

63% match

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.

63% match

Gist adbe358483e7e878175e

This example visualizes a network of places connected by paths, with each link representing a reciprocal route and its distance. The visualization shows the graph structure using a force-directed layout, where nodes are positioned based on their connections. The code loads relational data from a CSV file using Cypher’s `LOAD CSV`, then merges bidirectional `Path` relationships between `Place` nodes. The rendering relies on the Neo4j graph model and likely uses D3.js’s `forceSimulation` to compute node positions and link distances, though the snippet focuses on data ingestion via Cypher rather than the display layer. The graph reveals clusters of tightly interconnected locations based on the distance metadata.

61% match

GraphML for Neo4j

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.