Clifford Anderson's Scholarly Graph from Atlas

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.

71% 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.

70% match

Scholars at Vanderbilt with ORCIDs

This visualization maps the scholarly network of Vanderbilt University researchers who have ORCID identifiers, showing connections between 100 scholars based on their shared institutional affiliation. The graph uses d3.forceSimulation to position nodes and links, with each scholar represented as a point and their ORCID iD displayed as labels. Data is pulled live from Wikidata via a SPARQL query that filters for entries with both the Vanderbilt affiliation property and an ORCID identifier. The rendering approach emphasizes the clustering of researchers into a single academic community, with node positions dynamically updated by the simulation’s force-directed layout.

70% 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.

69% match

Artist Networks from WIkidata

This visualization maps the influence network of artists and thinkers from Wikidata, showing directed relationships from influencers to those they influenced. The code transforms the JSON data into a graph structure using XQuery, extracting entity IDs and labels to define nodes and edges. It uses the fetch and json:parse libraries to load and parse the data, then constructs Cypher-like statements for nodes and relationships. The resulting network highlights how figures like Copernicus and Strindberg connect to their intellectual predecessors.

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.

Gist ed756303e2cbb63f5fc8

This visualization uses a force-directed layout to show relationships between terms and facets, with each term linked to a facet (binding or year). The graph is rendered using D3’s `d3.forceSimulation`, with nodes and links drawn as SVG elements. The code reads from an XML file (`facets.xml`) via `d3.xml` and parses `<term>` elements to build the network. It applies a force simulation with charge and link forces, and updates node positions on each tick using `d3.drag` for interactivity. The visualization clearly distinguishes term nodes from facet nodes, revealing clustering by year and binding type.

force graph astroph

This example visualizes a directed force-directed graph representing co-authorship relationships among astrophysicists, with nodes for researchers and curved, arrowed links for collaborations weighted by the number of shared publications. The code loads data from a CSV file via `d3.csv`, builds the graph using D3 v3’s `d3.layout.force`, and renders nodes as SVG circles with text labels. Curved paths with arrow markers indicate link direction, and the simulation updates node positions and path geometry on each tick. The visualization adapts Mike Bostock’s classic force layout examples, using SVG for all rendering.

MMatteoTomassetti