Graph Analysis and Visualization : Discovering Business Opportunity in Linked Data.

By: Brath, Richard
Contributor(s): Jonker, David
Publisher: Somerset : John Wiley & Sons, Incorporated, 2015Copyright date: ©2015Edition: 1st edDescription: 1 online resource (539 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781118845691Subject(s): Network analysis (Planning);Graph theory -- Data processing.;Business -- Data processingGenre/Form: Electronic books. Additional physical formats: Print version:: Graph Analysis and Visualization : Discovering Business Opportunity in Linked DataDDC classification: 658.4032 LOC classification: T57.85Online resources: Click to View
Contents:
Intro -- Introduction -- Part 1: Overview -- Chapter 1: Why Graphs? -- Visualization in Business -- Graphs in Business -- Finding Anomalies -- Managing Networks and Supply Chains -- Identifying Risk Patterns -- Optimizing Asset Mix -- Mapping Social Hierarchies -- Detecting Communities -- Graphs Today -- Summary -- Chapter 2: A Graph for Every Problem -- Relationships -- Hierarchies -- Communities -- Flows -- Spatial Networks -- Summary -- Part 2: Process and Tools -- Chapter 3: Data-Collect, Clean, and Connect -- Know the Objective -- Collect: Identify Data -- Potential Graph Data Sources -- Potential Hierarchy Data Sources -- Getting the Data -- Clean: Fix the Data -- Connect: Organize Graph Data -- Compute the Graph -- Graph Data File Formats -- Putting It All Together -- Summary -- Chapter 4: Stats and Layout -- Basic Graph Statistics -- Size (Number of Nodes and Number of Edges) -- Density -- Number of Components -- Degree and Paths -- Centrality -- Viral Marketing Example -- Layouts -- Node-and-Link Layouts -- Other Layouts -- Force-Directed Layout -- Node-Only Layout -- Time Oriented -- Top-Down and Other Orthogonal Hierarchies -- Radial Hierarchy -- Geographic Layout and Maps -- Chord Diagrams -- Adjacency Matrix -- Treemap -- Hierarchical Pie Chart -- Parallel Coordinates -- Putting It All Together -- Summary -- Chapter 5: Visual Attributes -- Essential Visual Attributes -- Key Node Attributes -- Node Size -- Node Color -- Labels -- Key Edge Attributes -- Edge Weight -- Edge Color -- Edge Type -- Combining Basic Attributes -- Bundles, Shapes, Images, and More -- Bundled Edges -- Shape -- Node Image -- Node Border -- More Attributes -- Interference and Separation -- Putting It All Together -- Summary -- Chapter 6: Explore and Explain -- Explore, Explain, and Export -- Essential Exploratory Interactions.
Zoom and Pan (and Scale and Rotate…) -- Identify -- Filter -- Isolate and Redo Layout -- More Interactive Exploration -- Identifying Neighbors -- Paths -- Deleting -- Grouping -- Iterative Analysis -- Explain -- Sequence of a Data Story -- Legends -- Annotations -- Export Data Subsets, Graphs, and Images -- Putting It All Together -- Summary -- Chapter 7: Point-and-Click -- Excel -- Summarizing Links -- Extracting Nodes -- Adjacency Matrix Visualization in Excel -- NodeXL -- NodeXL Basics -- Social Network Features -- Gephi -- Gephi Basics -- Caveats -- Cytoscape -- Cytoscape Basics -- Importing Data into Cytoscape -- Visual Attributes -- Apps Menu -- yEd -- yEd Basics -- Summary -- Chapter 8: Lightweight ­Programming -- Python -- Getting Started -- Cleaning Data -- Extracting a Set of Nodes from a Link Data Set -- Transforming E‑mail Data into a Graph -- Graph Databases -- JavaScript and Graph Visualization -- D3 Basics -- D3 and Graphs -- D3 Springy Graph -- Summary -- Part 3: Visual Analysis -- Chapter 9: Relationships -- Links and Relationships -- Similarities in Fraud Claims -- Cybersecurity -- E‑mail Relationships -- Spatial Separation -- Actors and Movies -- Links Turned into Nodes -- Summary -- Chapter 10: Hierarchies -- Organizational Charts -- Trees and Graphs -- Drawing a Hierarchy -- Decision Trees -- Website Trees and Effectiveness -- Summary -- Chapter 11: Communities -- What Defines a Community? -- Graph Clustering -- A Social Network Case Study -- Social Media Using NodeXL and Gephi -- Layouts that Cluster -- Using Color to Characterize Clusters -- Community Detection -- Using Color to Distinguish Clusters -- Community Topic Analysis -- Community Sentiment -- Cliques and Other Groups -- Cliques in Social Media -- Community Groups with Convex Hulls -- Summary -- Chapter 12: Flows -- Sankey Diagrams -- Constructing a Sankey Diagram.
Create the Page Structure -- Process and Model the Data -- Visualize the Data -- Highlight Flow through a Node -- Community Layouts with Flow -- Chord Diagrams -- Constructing a Chord Diagram -- Prepare the Data -- Create the Page Structure -- Process and Model the Data -- Visualize the Data -- Interactive Details on Demand -- Behavioral Factor Tree -- Summary -- Chapter 13: Spatial Networks -- Schematic Layout -- A Modern Application -- Small World Grouping -- Link Rose Summaries -- Building a Link Rose Diagram -- Route Patterns -- Visualizing Route Segments -- Track Aggregation -- Summary -- Part 4: Advanced ­Techniques -- Chapter 14: Big Data -- Graph Databases -- A Product Marketing Example -- Creating and Populating a Graph Database -- Graph Query Languages -- Gremlin for Graph Queries -- Using Graph Queries to Extract Neighborhoods -- Analyzing Neighborhoods -- Topic Word Clouds -- Plotting Network Activity -- Community Visualization -- Summary -- Chapter 15: Dynamic Graphs -- Graph Changes -- Organic Animation -- Full Time Span Layout -- Ghosting -- Fading -- Community Evolution -- Transaction Graphs -- Clustered Transaction Analysis -- Spatial Transaction Analysis -- Summary -- Chapter 16: Design -- Nodes -- Node Shape -- Node Size -- Node Labels -- Links -- Link Shape -- Color -- Color Palettes -- Summary -- Glossary -- Index.
Summary: Wring more out of the data with a scientific approach to analysis Graph Analysis and Visualization brings graph theory out of the lab and into the real world. Using sophisticated methods and tools that span analysis functions, this guide shows you how to exploit graph and network analytic techniques to enable the discovery of new business insights and opportunities. Published in full color, the book describes the process of creating powerful visualizations using a rich and engaging set of examples from sports, finance, marketing, security, social media, and more. You will find practical guidance toward pattern identification and using various data sources, including Big Data, plus clear instruction on the use of software and programming. The companion website offers data sets, full code examples in Python, and links to all the tools covered in the book. Science has already reaped the benefit of network and graph theory, which has powered breakthroughs in physics, economics, genetics, and more. This book brings those proven techniques into the world of business, finance, strategy, and design, helping extract more information from data and better communicate the results to decision-makers. Study graphical examples of networks using clear and insightful visualizations Analyze specifically-curated, easy-to-use data sets from various industries Learn the software tools and programming languages that extract insights from data Code examples using the popular Python programming language There is a tremendous body of scientific work on network and graph theory, but very little of it directly applies to analyst functions outside of the core sciences - until now. Written for those seeking empirically based, systematic analysis methods and powerful tools that apply outside the lab, Graph Analysis and Visualization is a thorough, authoritativeSummary: resource.
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Intro -- Introduction -- Part 1: Overview -- Chapter 1: Why Graphs? -- Visualization in Business -- Graphs in Business -- Finding Anomalies -- Managing Networks and Supply Chains -- Identifying Risk Patterns -- Optimizing Asset Mix -- Mapping Social Hierarchies -- Detecting Communities -- Graphs Today -- Summary -- Chapter 2: A Graph for Every Problem -- Relationships -- Hierarchies -- Communities -- Flows -- Spatial Networks -- Summary -- Part 2: Process and Tools -- Chapter 3: Data-Collect, Clean, and Connect -- Know the Objective -- Collect: Identify Data -- Potential Graph Data Sources -- Potential Hierarchy Data Sources -- Getting the Data -- Clean: Fix the Data -- Connect: Organize Graph Data -- Compute the Graph -- Graph Data File Formats -- Putting It All Together -- Summary -- Chapter 4: Stats and Layout -- Basic Graph Statistics -- Size (Number of Nodes and Number of Edges) -- Density -- Number of Components -- Degree and Paths -- Centrality -- Viral Marketing Example -- Layouts -- Node-and-Link Layouts -- Other Layouts -- Force-Directed Layout -- Node-Only Layout -- Time Oriented -- Top-Down and Other Orthogonal Hierarchies -- Radial Hierarchy -- Geographic Layout and Maps -- Chord Diagrams -- Adjacency Matrix -- Treemap -- Hierarchical Pie Chart -- Parallel Coordinates -- Putting It All Together -- Summary -- Chapter 5: Visual Attributes -- Essential Visual Attributes -- Key Node Attributes -- Node Size -- Node Color -- Labels -- Key Edge Attributes -- Edge Weight -- Edge Color -- Edge Type -- Combining Basic Attributes -- Bundles, Shapes, Images, and More -- Bundled Edges -- Shape -- Node Image -- Node Border -- More Attributes -- Interference and Separation -- Putting It All Together -- Summary -- Chapter 6: Explore and Explain -- Explore, Explain, and Export -- Essential Exploratory Interactions.

Zoom and Pan (and Scale and Rotate…) -- Identify -- Filter -- Isolate and Redo Layout -- More Interactive Exploration -- Identifying Neighbors -- Paths -- Deleting -- Grouping -- Iterative Analysis -- Explain -- Sequence of a Data Story -- Legends -- Annotations -- Export Data Subsets, Graphs, and Images -- Putting It All Together -- Summary -- Chapter 7: Point-and-Click -- Excel -- Summarizing Links -- Extracting Nodes -- Adjacency Matrix Visualization in Excel -- NodeXL -- NodeXL Basics -- Social Network Features -- Gephi -- Gephi Basics -- Caveats -- Cytoscape -- Cytoscape Basics -- Importing Data into Cytoscape -- Visual Attributes -- Apps Menu -- yEd -- yEd Basics -- Summary -- Chapter 8: Lightweight ­Programming -- Python -- Getting Started -- Cleaning Data -- Extracting a Set of Nodes from a Link Data Set -- Transforming E‑mail Data into a Graph -- Graph Databases -- JavaScript and Graph Visualization -- D3 Basics -- D3 and Graphs -- D3 Springy Graph -- Summary -- Part 3: Visual Analysis -- Chapter 9: Relationships -- Links and Relationships -- Similarities in Fraud Claims -- Cybersecurity -- E‑mail Relationships -- Spatial Separation -- Actors and Movies -- Links Turned into Nodes -- Summary -- Chapter 10: Hierarchies -- Organizational Charts -- Trees and Graphs -- Drawing a Hierarchy -- Decision Trees -- Website Trees and Effectiveness -- Summary -- Chapter 11: Communities -- What Defines a Community? -- Graph Clustering -- A Social Network Case Study -- Social Media Using NodeXL and Gephi -- Layouts that Cluster -- Using Color to Characterize Clusters -- Community Detection -- Using Color to Distinguish Clusters -- Community Topic Analysis -- Community Sentiment -- Cliques and Other Groups -- Cliques in Social Media -- Community Groups with Convex Hulls -- Summary -- Chapter 12: Flows -- Sankey Diagrams -- Constructing a Sankey Diagram.

Create the Page Structure -- Process and Model the Data -- Visualize the Data -- Highlight Flow through a Node -- Community Layouts with Flow -- Chord Diagrams -- Constructing a Chord Diagram -- Prepare the Data -- Create the Page Structure -- Process and Model the Data -- Visualize the Data -- Interactive Details on Demand -- Behavioral Factor Tree -- Summary -- Chapter 13: Spatial Networks -- Schematic Layout -- A Modern Application -- Small World Grouping -- Link Rose Summaries -- Building a Link Rose Diagram -- Route Patterns -- Visualizing Route Segments -- Track Aggregation -- Summary -- Part 4: Advanced ­Techniques -- Chapter 14: Big Data -- Graph Databases -- A Product Marketing Example -- Creating and Populating a Graph Database -- Graph Query Languages -- Gremlin for Graph Queries -- Using Graph Queries to Extract Neighborhoods -- Analyzing Neighborhoods -- Topic Word Clouds -- Plotting Network Activity -- Community Visualization -- Summary -- Chapter 15: Dynamic Graphs -- Graph Changes -- Organic Animation -- Full Time Span Layout -- Ghosting -- Fading -- Community Evolution -- Transaction Graphs -- Clustered Transaction Analysis -- Spatial Transaction Analysis -- Summary -- Chapter 16: Design -- Nodes -- Node Shape -- Node Size -- Node Labels -- Links -- Link Shape -- Color -- Color Palettes -- Summary -- Glossary -- Index.

Wring more out of the data with a scientific approach to analysis Graph Analysis and Visualization brings graph theory out of the lab and into the real world. Using sophisticated methods and tools that span analysis functions, this guide shows you how to exploit graph and network analytic techniques to enable the discovery of new business insights and opportunities. Published in full color, the book describes the process of creating powerful visualizations using a rich and engaging set of examples from sports, finance, marketing, security, social media, and more. You will find practical guidance toward pattern identification and using various data sources, including Big Data, plus clear instruction on the use of software and programming. The companion website offers data sets, full code examples in Python, and links to all the tools covered in the book. Science has already reaped the benefit of network and graph theory, which has powered breakthroughs in physics, economics, genetics, and more. This book brings those proven techniques into the world of business, finance, strategy, and design, helping extract more information from data and better communicate the results to decision-makers. Study graphical examples of networks using clear and insightful visualizations Analyze specifically-curated, easy-to-use data sets from various industries Learn the software tools and programming languages that extract insights from data Code examples using the popular Python programming language There is a tremendous body of scientific work on network and graph theory, but very little of it directly applies to analyst functions outside of the core sciences - until now. Written for those seeking empirically based, systematic analysis methods and powerful tools that apply outside the lab, Graph Analysis and Visualization is a thorough, authoritative

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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