Email Enron Large Network

100%

Email Enron Large Network

✓ Published1🌍 Public

Last edited May 20, 2021

Created on May 20, 2021

A large Enron email network is visualized as a force-directed graph on a canvas. Nodes represent email addresses, and edges show communication links, with colors distinguishing parties. The layout is computed using d3.forceSimulation with forceX, forceY, forceCollide, forceManyBody, and forceLink forces, while drag interactions let users reposition nodes. Data is loaded from a local CSV file via d3.csv.

AI-generated description

A demonstration of how to start using D3 version 6.

MIT Licensed

Similar vizzes

Loading thumbnail…

Fork of Email Enron Large Network

This example visualizes a large email network from the Enron corpus using a force-directed graph rendered on a Canvas element. Nodes represent email addresses and links represent email exchanges, with node colors indicating party affiliation. The layout is computed with `d3.forceSimulation` and forces including `forceManyBody`, `forceCollide`, and `forceLink`, while user interaction enables dragging via `d3.drag`. Data is loaded from a CSV file, and rendering occurs on a 900x900 canvas with explicit draw functions.

PPeter Simeth

92% match

Loading thumbnail…

Force-Directed Hierarchy Graph with Node2Vec

This example visualizes a large email communication network as a force-directed hierarchy graph, using a Node2Vec-inspired random-walk embedding to compute node similarities. The view displays color-coded clusters of nodes, with edges connecting related individuals, allowing exploration of community structures within the Enron dataset. Built with D3 v7, it employs `d3.forceSimulation`, `d3.forceLink`, `d3.forceManyBody`, and `d3.forceCenter` to position nodes, while zoom and drag interactions are enabled via `d3.zoom` and `drag`. The graph is rendered as SVG, with node colors derived from either precomputed community assignments or k-means clustering on the embeddings.

KKhanin Sisaengsuwanchai

77% match

Loading thumbnail…

Arc diagram

This arc diagram visualizes email communication patterns within the Enron corporation, using data from a CSV file. It displays employee email addresses along a horizontal axis, with arcs connecting senders to recipients. The visualization employs D3.js (with SVG rendering) to create the arcs, scale point positioning for node layout, and ordinal color scaling to distinguish job titles. Node size and label rotation enhance readability, while the zoom functionality allows for detailed exploration of the network.

TTimStapert

74% match

Loading thumbnail…

Sorting data

This visualization displays the Enron email network as an adjacency matrix, where each cell represents the correspondence between a sender and recipient. The data is pre-processed by grouping emails by sender and receiver, calculating the total number of emails exchanged and the average sentiment score. The implementation uses d3.v4’s `d3.csv` and `d3.nest` APIs, along with custom JavaScript to structure the data before rendering the matrix. The example is built with the React framework, reading from a CSV file containing email metadata such as date, job titles, and sentiment values.

TTimStapert

73% match

Loading thumbnail…

Gist 8989f184ab98d1e190581e18829af3be

This example shows a force-directed graph of email communications, revealing clusters of senders and recipients by their connection strength. It uses D3’s `d3.forceSimulation` to compute node positions and `d3.forceLink` to manage edges, while a Canvas `getImageData` pixel readout colors nodes by their local density. The graph is drawn on a 2D canvas, with node size and color encoding degree centrality and community membership. Data is loaded from an external edge list, and the simulation runs continuously with a fade-in transition via `transition.attrTween` on opacity.

CConorAspell

73% match

Loading thumbnail…

Untitled

A circular arc diagram traces email communication among Enron employees, with nodes positioned along a horizontal axis and curved paths linking senders to recipients. Job titles determine node colors via a categorical scale, while node size reflects sender ID. The visualization filters the CSV dataset to 1,000 records, sorts by job title, and renders paths using SVG `path` elements with elliptical arcs. Built with D3 v4 and React, it supports zooming through `d3.zoom()` and relies on `d3.scalePoint` for positioning and `d3.scaleOrdinal` for color assignment from `d3.schemeSet3`.

TTimStapert

72% match

Loading thumbnail…

New matrix

This matrix visualization displays email communication patterns within the Enron dataset, showing the number of messages sent between employees as colored cells in an adjacency matrix. The code uses d3.nest() to group emails by sender and recipient, calculating total message counts and average sentiment scores for each pair. It renders the visualization using d3.v4 and d3-scale-chromatic for color mapping. The example demonstrates a data preprocessing workflow where nested data is flattened into link objects containing sender and recipient information, though the actual matrix rendering and node extraction remain incomplete.

TTimStapert

71% match

Loading thumbnail…

Data manipulation trial

This visualization demonstrates the initial stages of transforming raw Enron email data into an interactive adjacency matrix. It shows how email records are grouped and aggregated to reveal communication patterns and sentiment scores between individuals. The code uses D3.js v3’s `d3.nest()` and `d3.csv()` to parse the dataset, then restructures grouped data into link objects containing sender, recipient, mail count, and average sentiment. Key D3 API methods include `d3.sum()` and `d3.mean()` for rollup calculations, alongside standard JavaScript array methods to prepare nodes for future rendering.

TTimStapert

71% match