Book Review: Analyzing Social Media Networks with NodeXL – Appendix

Vertices

nodes, agents, entities, items

Building blocks of networks, can represent many things:

• people or social structures such as workgroups, teams, organizations, institutions, states, or even countries
• content: web pages, keyword tags, or videos.
• physical or virtual locations or events

Attribute data may describe demographic characteristics of a person (age, gender, race), data that describe the person's use of a system (number of logins, messages posted, edits made) or other characteristics such as income or location.

In network visualization tools such as NodeXL, attribute data can be mapped to visual properties such as the size, color, or opacity of the vertices

Edges

links, ties, connections, relationships

Building blocks of networks; an edge connects two vertices together. Edges can represent many different types of relationships:

• proximity
• collaborations
• kinship, friendship, trade partnership
• citations
• investments
• hyperlinking
• transactions
• shared attributes

Types of edges:

• Undirected or directed
  • Undirected (or symmetric) edges simply exists between two people or things
  • Directed (or asymmetric) edges have a clear origin and destination
• Unweighted or weighted
  • Unweighted edge or binary edge: Indicates only if an edge exists or not
  • Weighted edge: Includes values associated with each edge that indicate the strength or frequency of a tie

Directed edges are represented on a graph as a line with an arrow pointing from the source vertex to the recipient vertex. Undirected edges are represented on a graph as a line connecting two vertices with no arrows.

Weighted edges are often represented visually as thicker or darker lines or as more or less opaque lines.

Only include individuals who are connected to a specified ego. More generally, egocentric networks can extend out any number of "degrees" from ego. Types of ecocentric networks:

• 1-degree ego network: Consists of the ego and their alters.
• 1.5-degree ego network: Extends the 1-degree network by including connections between all of the alters.
• 2-degree ego network: Extends the 1.5-degree network by including all of the alters' own alters (i.e., friends of friends), some of whom may not be connected to ego.

Aggregate Networks Metrics

This set of metrics describe entire networks

Metrics used to describe the level of interconnectedness of the vertices.

Density is a count of the number of relationships observed to be present in a network divided by the total number of possible relationships that could be present.

Metrics that characterizes the amount to which the network is centered on one or a few important nodes.

Centralized networks have many edges that emanate from a few important vertices, whereas decentralized networks have little variation between the numbers of edges each vertex possesses.

Vertex-Specific Networks Metrics

This set of metrics identifies individuals' positions within a network

The distance between people who are not neighbors is measured by the smallest number of neighbor-to-neighbor hops from one to the other. The shortest path between two people is called the "geodesic distance" and is used in many centrality metrics.

Betweenness centrality is a measure of how often a given vertex lies on the shortest path between two other vertices. This can be thought of as a kind of "bridge" score, a measure of how much removing a person would disrupt the connections between other people in the network (idea of brokering). A "structural" hole is a missing bridge. Wherever two or more groups fail to connect, one can argue that there is a structural hole, a missing gap waiting to be filled.

The average shortest distance between a vertex and every other vertex in the network (measures how close a person is to every other person the network; closeness is paradoxically a "distance" score).

In some cases the inverse of the average distance to others in the network is used as a measure of closeness centrality. In that case, higher values indicate a more central position.

Part I: Getting Started with Analyzing Social Media Networks

1. Introduction to Social Media and Social Networks

2. Social Media: New Technologies of Collaboration

3. Social Network Analysis: Measuring, Mapping, and Modeling Collections of Connections

Part II: NodeXL Tutorial – Learning by Doing

4. Getting Started with NodeXL, Layout, Visual Design, and Labeling

5. Calculating and Visualizing Network Metrics

6. Preparing Data and Filtering

7. Clustering and Grouping

Part III: Social Media Network Analysis Case Studies

8. Email: The Lifeblood of Modern Communication

9. Thread Networks: Mapping Message Boards and Email Lists

10. Twitter: Conversation, Entertainment, and Information, All in One Network! (by Vladimir Barash and Scott Golder)

11. Visualizing and Interpreting Facebook Networks (by Bernie Hogan)

12. WWW Hyperlink Networks (by Robert Ackland)

13. Flickr: Linking People, Photos, and Tags (by Eduarda Mendes Rodrigues and Natasa Milic-Frayling)

14. YouTube: Contrasting Patterns of Content, Interaction, and Prominence (by Dana Rotman and Jennifer Golbeck)

15. Wiki Networks: Connections of Creativity and Collaboration (by Howard T. Welser, Patrick Underwood, Dan Cosley, Derek Hansen, and Laura W. Black)

Appendix: NodeXL for Programmers (by Tony Capone)

Index

Shows how users with the respective background in programming can customize the NodeXL Excel 2007 template to import network graph data from any data source and create own graphing applications using the NodeXL class libraries.

Circle

Spiral

Grid

Fruchterman-Rheingold

Harel-Koren

Harel-Koren after refresh