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Analyzing social media networks with NodeXL - Chapter-07 Images

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Figures and images from the book: _Analyzing social media networks with NodeXL: Insights from a connected world_

Figures and images from the book: _Analyzing social media networks with NodeXL: Insights from a connected world_

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  • The University of Adelaide, School of Computer Science October 31, 2010 Chapter 2 — Instructions: Language of the Computer

Analyzing social media networks with NodeXL - Chapter-07 Images Analyzing social media networks with NodeXL - Chapter-07 Images Presentation Transcript

  • Copyright © 2011, Elsevier Inc. All rights Reserved Chapter 7 Clustering and Grouping Analyzing Social Media Networks with NodeXL Insights from a Connected World
  • FIGURE 7.1 Copyright © 2011, Elsevier Inc. All rights Reserved Chapter 7 A network of three densely connected clusters, each shown inside a dashed circle. Ties between clusters are rare and less dense.
  • FIGURE 7.2 Copyright © 2011, Elsevier Inc. All rights Reserved Chapter 7 Unfiltered 2007 U.S. Senate co-voting network showing all 48 senators connected to each other. Other columns in the NodeXL Edges worksheet show the number of times each pair of senators voted the same and their percentage agreement. A weak tie (Akaka and Allard) and a strong tie (Akaka and Baucus) are highlighted. “Raw” visualizations like this require refinement to display useful insights.
  • FIGURE 7.3 Copyright © 2011, Elsevier Inc. All rights Reserved Chapter 7 The NodeXL Autofill Columns window with Edge Visibility Options set to “Greater Than 0.65” and Edge Opacity Options set to a range of 0.65 (edge opacity 10) to “The largest number in the column” (edge opacity 100).
  • FIGURE 7.4 Copyright © 2011, Elsevier Inc. All rights Reserved Chapter 7 The 2007 U.S. Senate co-voting network graph after using Autofill Columns (see Fig. 7.3), setting Vertex Shape to Label, and Edge Color to (128, 128, 192). The Fruchterman-Reingold Layout visually creates two clear groups with a few boundary spanners in the middle after enough iterations.
  • FIGURE 7.5 Copyright © 2011, Elsevier Inc. All rights Reserved Chapter 7 NodeXL Layout Options window used to increase the repulsive force between vertices, helping spread out vertices to reduce overlap.
  • FIGURE 7.6 Copyright © 2011, Elsevier Inc. All rights Reserved Chapter 7 The 2007 U.S. Senate co-voting network after applying the Find Clusters feature and choosing to view the Graph Element – Clusters from the NodeXL ribbon. Each of the three automatically identified clusters is given a name, color, and shape on the Clusters worksheet.
  • FIGURE 7.7 Copyright © 2011, Elsevier Inc. All rights Reserved Chapter 7 The NodeXL Cluster Vertices worksheet that maps each vertex into exactly one cluster. For example, Collins is the only member of Cluster C14, whereas Specter, Smith, and the others are members of Cluster C143.
  • FIGURE 7.8 Copyright © 2011, Elsevier Inc. All rights Reserved Chapter 7 The Cluster Vertices worksheet used to manually map each vertex (senators) to a cluster (political party).
  • FIGURE 7.9 Copyright © 2011, Elsevier Inc. All rights Reserved Chapter 7 The NodeXL Clusters worksheet showing the four unique clusters and their associated colors and shapes: Democrats (D) in blue, Republicans (R) in red, and Independents (I) and Independent Democrats (ID) in dark orange.
  • FIGURE 7.10 Copyright © 2011, Elsevier Inc. All rights Reserved Chapter 7 Les Misérables character co-appearance network with automatically identified clusters represented by unique color and shape combinations.
  • FIGURE 7.11 Copyright © 2011, Elsevier Inc. All rights Reserved Chapter 7 Lobbying Coalition network connecting organizations (vertices) that have jointly filed comments on U.S. Federal Communications Commission policies (edges). Vertex size represents number of filings and color represents eigenvector centrality (pink higher). Darker edges connect organizations with many joint filings. Vertices were originally positioned using Fruchterman-Reingold and handpositioned to respect clusters identified by NodeXL’s Find Clusters algorithm.