Visualizing Graph Data
Corey L. Lanum
  • November 2016
  • ISBN 9781617293078
  • 232 pages
  • printed in black & white

Shows you how to solve visualization problems and explore complex data sets. A pragmatic introduction.

John D. Lewis, DDN

Visualizing Graph Data teaches you not only how to build graph data structures, but also how to create your own dynamic and interactive visualizations using a variety of tools. This book is loaded with fascinating examples and case studies to show you the real-world value of graph visualizations.

About the Technology

Assume you are doing a great job collecting data about your customers and products. Are you able to turn your rich data into important insight? Complex relationships in large data sets can be difficult to recognize. Visualizing these connections as graphs makes it possible to see the patterns, so you can find meaning in an otherwise over-whelming sea of facts.

About the book

Visualizing Graph Data teaches you how to understand graph data, build graph data structures, and create meaningful visualizations. This engaging book gently introduces graph data visualization through fascinating examples and compelling case studies. You'll discover simple, but effective, techniques to model your data, handle big data, and depict temporal and spatial data. By the end, you'll have a conceptual foundation as well as the practical skills to explore your own data with confidence.

Table of Contents detailed table of contents

Part 1: Graph Visualization Basics

1. Introduction to Graph Visualization

1.1. Get To Know Graphs

1.1.1. What Is a Graph

1.1.2. A bit of theory

1.1.3. The Graph Data Model Introduced

1.1.4. When are Graphs helpful?

1.2. Get to know Graph Visualiztion

1.2.1. When to Visualize Graphs

1.2.2. Alternative Graph Visualizations

1.3. Summary

2. Case Studies in Graph Visualization

2.1. Intelligence and Terrorism

2.2. Credit Card Fraud

2.2.1. The Markers of Online Shopping Fraud

2.2.2. Online Review Fraud

2.2.3. Visualizing Review Fraud

2.3. Cyber Security

2.3.1. Understanding Unusual Network Traffic

2.3.2. Deconstructing a Botnet Attack

2.3.3. Analyzing Malware Propagation

2.4. Sales and Marketing Graphs

2.5. Summary

3. An Introduction to Gephi and KeyLines

3.1. Gephi

3.1.1. Acquiring Data

3.1.2. Importing Data into Gephi

3.1.3. Visually Organizing the data with Layouts

3.1.4. Know What You are Looking at With Labels

3.1.5. Filtering

3.1.6. Size

3.1.7. Color

3.1.8. Final Product

3.2. KeyLines

3.2.1. Encode an HTML Page

3.2.2. Write KeyLines JavaScript

3.2.3. Bind KeyLines to Data

3.3. Summary

Part 2: Visualize Your Own Data

4. Data Modeling

4.1. What Is A Data Model?

4.1.1. Relational Data

4.1.2. Key-Value Stores

4.2. Graph Data Models

4.2.1. Identifying Nodes

4.3. Graph Databases

4.3.1. Neo4j

4.3.2. Titan

4.4. Summary

5. Engage Your Audience: How To Build Graph Visualizations

5.1. Understand Your User

5.2. Use Intuitive Visual Properties

5.2.1. Size

5.2.2. Color

5.2.3. Node Icons

5.2.4. Glyphs

5.2.5. Labels

5.3. Build Charts with Visual Properties

5.4. Summary

6. Creating Interactive Visualizations

6.1. Chart Navigation

6.2. Declutter Your Charts

6.2.1. Implementation in Gephi

6.2.2. Implementation in KeyLines

6.3. Data Volumes

6.3.1. Expanding Nodes to Add Data

6.4. Animations And Mobile

6.4.1. Animating Charts

6.4.2. Designing for Mobile Touch Environments

6.5. Summary

7. Graph Layouts: How to Organize a Chart

7.1. The Force Directed Layouts

7.1.1. Force Directed Layouts in Gephi

7.1.2. Implementation in KeyLines

7.2. Other Layout Options

7.2.1. Circular Layouts

7.2.2. Hierarchy Layout

7.2.3. Radial Layout

7.2.4. 3D Layouts

7.3. Summary

8. Big Data: Using Graphs When There Is Too Much Data

8.1. Controlling Which Nodes and Edges Are Visible

8.1.1. Filtering in Gephi

8.1.2. Filtering in KeyLines

8.2. Grouping and Combinations

8.2.1. What Is Grouping?

8.2.2. Grouping in Gephi

8.2.3. Grouping in KeyLines

8.3. Summary

9. Dynamic Graphs: How to Show Data Over Time

9.1. How Do Graphs Change Over Time?

9.2. How to Visualize Changes over Time

9.2.1. Small Multiples — Showing Time With Many Small Graphs

9.2.2. Time—Based Filtering

9.2.3. Graphs with Dynamic Properties

9.3. Implementing Dynamic Graphs

9.3.1. Dynamic Graphs in Gephi

9.3.2. Dynamic Graphs in KeyLines

9.4. Summary

10. Graphs on Maps: The Where of Graph Visualization

10.1. Working With Geographical Data

10.1.1. Graphs With Location Data

10.1.2. How Can We Model Locations in a Graph

10.1.3. Limitations to Representing Locations as a Property on a Node

10.2. Overlaying Graphs on Maps

10.2.1. Filtering to Subsets of the Data

10.2.2. Combinations or Grouping

10.3. Building Graphs on Maps

10.3.1. Storing the Data in the KeyLines Object Model

10.3.2. Building an Example from the Hubway Data

10.4. Summary


11. An Introduction to D3.js

11.1. A Very Brief Introduction to D3.js

11.1.1. Selectors

11.1.2. Data Formats

11.1.3. SVG Drawing

11.1.4. Interactivity

11.1.5. Graphs in D3

11.2. Building a Graph in D3.js

11.3. Summary

What's inside

  • Techniques for creating effective visualizations
  • Examples using the Gephi and KeyLines visualization packages
  • Real-world case studies

About the reader

No prior experience with graph data is required.

About the author

Corey Lanum has decades of experience building visualization and analysis applications for companies and government agencies around the globe.

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