Skip to main content
100%

Gist 2768d4a5ea98e385b30ffc99390cddb7

✓ Published0🌍 Public
Aakollegger
Last edited Apr 19, 2023
Created on Apr 19, 2023

This example demonstrates a custom JSON encoder for serializing graph data structures. It shows how Python’s `json.JSONEncoder` is extended to convert `graph.Node` and `graph.Relationship` objects into a plain dictionary format, capturing element IDs, labels, properties, and relationship endpoints. The encoder uses `json.dumps` under the hood but overrides the `default` method to handle graph-specific types. The code relies on the `graph` module’s `Node` and `Relationship` classes, and its output is a JSON-friendly representation suitable for further visualization or data exchange.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 909583a7882aa8a80ef2bb129b27d2d4

This example demonstrates a grammar for describing graph structures, including nodes, relationships, and paths, using a compact textual notation. It defines a node with a label and property, a relationship between two nodes, and a path that traverses multiple relationships through intermediate nodes. The code likely parses this grammar to generate a visual graph representation, though the rendering logic is not shown. It uses a custom grammar format rather than a standard visualization library, focusing on the syntactic layer for graph data description.

Aakollegger
68% match
Loading thumbnail…

Gist 3901827

This visualization shows a graph of user connections from the GitHub API, filtered by the login name “akollegger.” The code queries nodes by the `users` index and returns the starting node, rendering it as a simple network diagram. It uses a Neo4j Cypher query to fetch the data, but no visualization library is yet applied—the output appears as raw graph data rather than a graphical layout. The example demonstrates how to initiate a graph traversal from a specific indexed user node, with the rendering approach left unspecified in the provided code.

Aakollegger
61% match
Loading thumbnail…

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.

CCliffordAnderson
58% match
Loading thumbnail…

Gist 2b1e40cab16412108ada

This example demonstrates a force-directed graph visualization rendered as a web page. It shows a network of nodes and links arranged through a physics simulation, with the layout computed dynamically as the simulation runs. The code uses D3.js’s `d3.forceSimulation` to manage node positions and link forces, while the visualization updates in real time on an HTML canvas. The data source is a hardcoded set of nodes and edges defined in the script, and the rendering approach relies on canvas drawing commands rather than SVG. The example also includes a `sitecustomize.py` file to set UTF-8 encoding for the Python environment, though that file does not affect the visualization itself.

Aandybak
58% match
Loading thumbnail…

Gist 099e4d37161b6a93c9f60f524bb8382c

This example demonstrates how to load and persist parameterized records into a Neo4j graph database using the official Neo4j Python driver. It shows a small dataset of four people, each tagged with a data source identifier, being merged into the database as `Record` nodes with unique IDs constructed from the source and record number. The code establishes a connection via `neo4j.GraphDatabase.driver`, iterates through the sample data, and executes a Cypher query with `session.run` to upsert each record, using `ON CREATE SET` to add properties only when the node is newly created. The visualization would reflect the resulting graph structure, though no rendering library is used here.

Aakollegger
58% match
Loading thumbnail…

Gist f09ab9588a2aa90a6f534845a85d43b8

This example presents a single linked list data structure implemented in Python, demonstrating node traversal, insertion, search, and removal operations. The code defines a `Node` class to store values and references, while the `LinkList` class manages the head node and provides methods such as `push`, `pop`, `search`, and `out_put`. Traversal logic moves through the list iteratively, and the `out_put` method prints each value to the console. The visualization is purely code-based, with no graphical rendering or external libraries.

Aaarsenault
57% match
Loading thumbnail…

Gist 6db8115f185f7e57cb4395da0474fbf6

This example presents an interactive visualization of a network graph, where nodes are connected by curved links and can be dragged by the user. The code builds the layout using `d3.forceSimulation` for physics-based positioning and renders the scene with `d3.select` and SVG elements, including `transition.attrTween` for smooth link animations. It reads a JSON dataset of node and edge data from an external file, and uses `d3.zoom` for panning and scaling the view. The visualization shows how force-directed algorithms distribute connected nodes while user input adjusts the simulation in real time.

11wheel
57% match
Loading thumbnail…

Gist 41688f44df38ac0b35d9

This example shows a force-directed network graph where nodes are connected by curved links, with a color scheme that differentiates clusters. The visualization uses D3.js’s `forceSimulation` API to compute the physics-based layout, while the links are drawn as Bézier curves using the `d3.geoPath` generator. Node positions and link paths are updated iteratively in a custom simulation loop, with the rendering performed on an HTML5 Canvas element. The code reads a static JSON dataset embedded within the HTML file, providing the node and link definitions for the graph.

MMyoshu
57% match