CS-GY 6313: Information Visualization
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1.0.0
  • Introduction
  • Defining Information Visualization
  • Why Use Visualization?
  • Popular Visualization Sources and Tools
  • Why Use a Graphical Representation?
  • The Problem with Statistics
  • Why Use a Computer to Visualize Data?
  • Why Use Interaction?
  • Assessing the Quality of a Visualization
  • Data Abstraction
    • Types of Datasets
    • Types of Attributes
    • Attribute Semantics
    • Data Abstraction to Visualization
    • Data Profiling
  • Fundamental Graphs
    • Alternate Representations
    • Visualizing More Than 2 Attributes
    • Faceting
  • Data Transformation
  • Graphical Components and Mapping Strategies
    • Marks
    • Channels
    • Graphical Decoding
    • Evaluating the Quality of a Visual Encoding
    • Contextual Components
  • Color
    • Color Perception
    • Color Specification
    • Color Use
      • Quantitative Color Scales
      • Categorical Color Scales
      • Diverging Color Scales
      • Highlighting
    • Perceptual Issues with Color
  • Geo Visualization
    • When to Use Maps
    • Geo Visualization Techniques
      • Dot Maps
      • Heat Maps
      • Hexbin Maps
      • Choropleth Maps
      • Graduated Symbol Maps
      • Summary of Map Types
    • Issues with Maps
    • Visualizing Geo Data with Time
  • Visualizing Temporal Data
    • Time Structures
    • Visualization Methods
    • Increasing Visual Scalability
    • Beyond Using Position
  • Networks and Trees
    • Visualizing Network Data
      • Node-Link Diagrams
        • Clutter Reduction
      • Matrices
    • Visualizing Trees
      • Node-Link Diagrams
      • Special Kinds of Trees
      • Space-Partitioning and Containment
        • Sunburst and Icicle Plots
  • Interaction and Multiple Views
    • Single and Multiple View Methods
      • Single View Methods
      • Multiple (Linked) Views Methods
    • Common Scenarios
  • Exploring Data
  • Animation, Pacing and Exposition
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  • Why Use Matrices?
  • Drawbacks
  • Note

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  1. Networks and Trees
  2. Visualizing Network Data

Matrices

PreviousClutter ReductionNextVisualizing Trees

Last updated 5 years ago

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Matrices are another way to visualize network data.

This is done using an adjacency matrix.

The nodes are encoded using rows and columns, and the edges are encoded in the cells.

Why Use Matrices?

  • Full visibility of nodes and relationships, and corresponding properties

  • No line crossings (so, no clutter)

    This gets rid of the hairball effect that is common in node-link diagrams, especially when every node is connected to every other node:

Drawbacks

  • Less familiarity with the representation, since it isn't very common

  • Feels less natural than using nodes and links

  • Less scalability (cells become too small beyond 100-200 nodes)

  • Needs reordering in order to show structures or patterns

Note

For undirected networks, matrices are symmetric. However, for directed networks, the cells can be filled according to the edge directions.

Left: without sorting the data, Right: after sorting the data

A parallel axes representation is a common substitute for matrices, when direction is to be shown: