ISBN-10:
1118845846
ISBN-13:
9781118845844
Pub. Date:
02/02/2015
Publisher:
Wiley
Graph Analysis and Visualization: Discovering Business Opportunity in Linked Data / Edition 1

Graph Analysis and Visualization: Discovering Business Opportunity in Linked Data / Edition 1

by Richard Brath, David Jonker
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Product Details

ISBN-13: 9781118845844
Publisher: Wiley
Publication date: 02/02/2015
Pages: 544
Product dimensions: 7.40(w) x 9.00(h) x 1.10(d)

About the Author

Richard Brath is actively involved in the research,design, and development of data visualization and visual analyticsfor both research and commercial applications for some of theworld’s largest companies and top software vendors. Hissolutions range from rich, interactive visualizations for mobiledevices, to large multi-touch, multi-screen installations andweb-based analytical visualizations for business applications.David Jonker is a designer and developer of visualizationplatforms and applications for web-based, distributed, desktop, andmobile use. He created visualization products for NASDAQ MarketSitereal-time broadcast in Times Square, and is currently a lead on theDARPA XDATA program, developing new tools and technologies formaking sense of Big Data. Brath and Jonker are partners at aleading visual analytics firm. Between them they have presented atmore than 30 industry conferences, and have published numerousarticles and research papers.

Table of Contents

Introduction xvii

Part 1 Overview

Chapter 1 Why Graphs? 3

Visualization in Business 4

Graphs in Business 7

Finding Anomalies 9

Managing Networks and Supply Chains 11

Identifying Risk Patterns 15

Optimizing Asset Mix 18

Mapping Social Hierarchies 20

Detecting Communities 22

Graphs Today 25

Summary 26

Chapter 2 A Graph for Every Problem 27

Relationships 28

Hierarchies 32

Communities 36

Flows 40

Spatial Networks 45

Summary 49

Part 2 Process and Tools

Process 52

Tools 53

Chapter 3 Data-Collect, Clean, and Connect 55

Know the Objective 56

Collect: Identify Data 56

Potential Graph Data Sources 57

Potential Hierarchy Data Sources 65

Getting the Data 67

Clean: Fix the Data 69

Connect: Organize Graph Data 71

Compute the Graph 73

Graph Data File Formats 75

Putting It All Together 85

Summary 85

Chapter 4 Stats and Layout 87

Basic Graph Statistics 88

Size (Number of Nodes and Number of Edges) 88

Density 88

Number of Components 89

Degree and Paths 90

Centrality 93

Viral Marketing Example 95

Layouts 97

Node-and-Link Layouts 97

Other Layouts 98

Force-Directed Layout 99

Node-Only Layout 106

Time Oriented 107

Top-Down and Other Orthogonal Hierarchies 109

Radial Hierarchy 111

Geographic Layout and Maps 112

Chord Diagrams 114

Adjacency Matrix 115

Treemap 117

Hierarchical Pie Chart 118

Parallel Coordinates 118

Putting It All Together 122

Summary 123

Chapter 5 Visual Attributes 125

Essential Visual Attributes 127

Key Node Attributes 129

Node Size 129

Node Color 132

Labels 137

Key Edge Attributes 143

Edge Weight 143

Edge Color 144

Edge Type 144

Combining Basic Attributes 146

Bundles, Shapes, Images, and More 148

Bundled Edges 148

Shape 148

Node Image 149

Node Border 150

More Attributes 151

Interference and Separation 152

Putting It All Together 153

Summary 155

Chapter 6 Explore and Explain 157

Explore, Explain, and Export 158

Essential Exploratory Interactions 160

Zoom and Pan (and Scale and Rotate…) 162

Identify 164

Filter 166

Isolate and Redo Layout 168

More Interactive Exploration 171

Identifying Neighbors 171

Paths 173

Deleting 174

Grouping 176

Iterative Analysis 176

Explain 177

Sequence of a Data Story 178

Legends 180

Annotations 181

Export Data Subsets, Graphs, and Images 183

Putting It All Together 185

Summary 186

Chapter 7 Point-and-Click Graph Tools 187

Excel 188

Summarizing Links 188

Extracting Nodes 190

Adjacency Matrix Visualization in Excel 190

NodeXL 193

NodeXL Basics 103

Social Network Features 196

Gephi 201

Gephi Basics 201

Caveats 205

Cytoscape 208

Cytoscope Basics 209

Importing Data into Cytoscape 210

Visual Attributes 212

Apps Menu 218

yEd 218

yEd Basics 219

Summary 222

Chapter 8 Lightweight Programming 223

Python 224

Getting Started 224

Cleaning Data 225

Extracting a Set of Nodes from a Link Data Set 227

Transforming E-mail Data into a Graph 233

Graph Databases 241

JavaScript and Graph Visualization 242

D3 Basics 242

Do and Graphs 250

D3 Springy Graph 264

Summary 272

Part 3 Visual Analysis of Graphs

Chapter 9 Relationships 275

Links and Relationships 276

Similarities in Fraud Claims 277

Cyber Security 279

E-mail Relationships 282

Spatial Separation 283

Actors and Movies 286

Links Turned into Nodes 290

Summary 292

Chapter 10 Hierarchies 293

Organizational Charts 293

Trees and Graphs 297

Drawing a Hierarchy 300

Decision Trees 306

Website Trees and Effectiveness 309

Summary 314

Chapter 11 Communities 315

What Defines a Community? 317

Graph Clustering 318

A Social Network Case Study 319

Social Media Using NodeXL and Gephi 320

Layouts that Cluster 323

Using Color to Characterize Clusters 326

Community Detection 328

Using Color to Distinguish Clusters 330

Community Topic Analysis 334

Community Sentiment 338

Cliques and Other Groups 342

Cliques in Social Media 343

Community Groups with Convex Hulls 345

Summary 348

Chapter 12 Flows 351

Sankey Diagrams 352

Constructing a Sankey Diagram 356

Create the Page Structure 357

Process and Model the Data 358

Visualize the Data 358

Highlight Flow through a Node 362

Community Layouts with Flow 364

Chord Diagrams 367

Constructing a Chord Diagram 369

Prepare the Data 370

Create the Page Structure 371

Process and Model the Data 372

Visualize the Data 376

Interactive Details on Demand 382

Behavioral Factor Tree 384

Summary 387

Chapter 13 Spatial Networks 389

Schematic Layout 390

A Modern Application 393

Small World Grouping 397

Link Rose Summaries 398

Building a Link Rose Diagram 401

Route Patterns 408

Visualizing Route Segments 410

Track Aggregation 414

Summary 415

Part 4 Advanced Techniques

Chapter 14 Big Data 419

Graph Databases 421

A Product Marketing Example 422

Creating and Populating a Graph Database 424

Graph Query Languages 427

Gremlin for Graph Queries 428

Using Graph Queries to Extract Neighborhoods 432

Analyzing Neighborhoods 435

Topic Word Clouds 441

Plotting Network Activity 444

Community Visualization 446

Summary 448

Chapter 15 Dynamic Graphs 449

Graph Changes 450

Organic Animation 450

Full Time Span Layout 454

Ghosting 455

Fading 457

Community Evolution 458

Transaction Graphs 461

Clustered Transaction Analysis 461

Spatial Transaction Analysis 469

Summary 472

Chapter 16 Design 473

Nodes 474

Node Shape 475

Node Size 484

Node Labels 485

Links 486

Link Shape 486

Color 492

Color Palettes 492

Summary 496

Glossary 497

Index 501

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