Influential Artists

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.

83% match

Projet 4

This visualization displays a network of recommended music artists based on association rule mining. The force-directed graph shows artist nodes connected by links, where the strength of each connection represents the support value from the dataset. Using D3 v4, the layout employs a force simulation to position nodes, with link opacity reflecting relationship strength. The data comes from a CSV file containing antecedent-consequence pairs with support counts, and the interactive visualization also includes a searchable autocomplete feature for finding specific artists in the network.

MMelanieSalignat

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.

Projet 2

This visualization shows a force-directed network of music artists and their associations, mapping relationships derived from an association-rule mining dataset where nodes represent artists and links indicate co-occurrence strength. The graph uses `d3.forceSimulation` with collision and link forces to position nodes, with circle size encoding node degree and line opacity reflecting link weight. Hovering a node highlights its connections, and the code includes an autocomplete search feature that filters the network. Data is loaded from CSV files containing artist tags and recommendation pairs, rendered as SVG elements with tooltips for interactivity.

MMelanieSalignat

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.

Projet_test3

This example visualizes a network of music artist recommendations, showing connections between artists based on association rule mining. An interactive force-directed graph places artists as nodes, with links representing the strength of their co-occurrence. Hovering over a node highlights it, and a tooltip appears; the code also implements a function to dynamically generate a list of five recommended artists for each selected node. The visualization uses D3.js v4 APIs including `d3.forceSimulation`, `d3.forceLink`, `d3.forceManyBody`, and `d3.forceCenter`, with data loaded from CSV files (`recommandations.csv` and `artists_tags_count.csv`). Artist images and top five tags are integrated from the latter source, enhancing node details.

BBrunoDumas

64% match

Projet : Artists recommendation

This visualization presents a force-directed network of music artist recommendations, derived from association rule mining in `recommandations.csv`. Nodes represent individual artists, while weighted links depict the strength of their co-occurrence in recommendation rules. The network uses a physical simulation engine, combining `d3.forceSimulation` with `d3.forceLink`, `d3.forceManyBody`, and `d3.forceCenter` forces to arrange the graph. Upon hovering over a node, the code highlights that artist and all directly recommended artists by increasing their radius and changing their color, with a custom tooltip displaying the artist's name. The visualization is rendered as an SVG using D3 v4.

MMelanieSalignat

64% 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.