This document summarizes a presentation about using NodeXL to visualize social network data from Twitter, MapBox, and other sources. It introduces NodeXL as a free and easy-to-use tool for social network analysis built on Excel. The presentation discusses key concepts in social network theory like nodes, edges, centrality, and clustering. It then shows examples of using NodeXL to analyze innovation networks and visualize data from CrunchBase on capital flows between startups, accelerators, and investors. The purpose is to demonstrate how network analysis and visualization with NodeXL can provide insights into clusters and connections in social and innovation ecosystems.

1. Leveraging NodeXL
for Visualization
of Social Network Data
Visualizing Social Data with Twitter, MapBox, and NodeXL
C. Scott Dempwolf, PhD
Research Assistant Professor
& Director
Social Data and Analytics Meetup
The Washington Post
August 19, , 2014
UMD – Morgan State
Center for Economic Development

2. Who are you ?
(and why are you staring at me?)
Who you were yesterday… Who you are today…

3. Who am I?
(and how did I get here?)
• At UMD since 2007
– 2007 – 1012 PhD student
– 2012 - Research Asst.
Professor
• Using NodeXL since 2011
• Uses of Social Network
Analysis in Planning
• Focus on innovation &
economic development

4. Social Network Theory
In one slide
• Central tenet
– Social structure emerges from
– the aggregate of relationships (ties)
– among members of a population
• Phenomena of interest
– Emergence of cliques and clusters
– from patterns of relationships
– Centrality (core), periphery (isolates),
– betweenness
• Methods
– Surveys, interviews, observations,
log file analysis, computational
analysis of matrices
(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)
http://en.wikipedia.org/wiki/Social_network
Source: Richards, W.
(1986). The NEGOPY
network analysis
program. Burnaby, BC:
Department of
Communication, Simon
Fraser University. pp.7-
16

5. OK… two slides
• Node
– “actor” on which relationships act; 1-mode versus 2-mode networks
• Edge
– Relationship connecting nodes; can be directional
• Cohesive Sub-Group
– Well-connected group; clique; cluster
• Key Metrics
– Centrality (group or individual measure)
A B D E
• Number of direct connections that individuals have with others in the group (usually look at
incoming connections only)
• Measure at the individual node or group level
– Cohesion (group measure)
• Ease with which a network can connect
• Aggregate measure of shortest path between each node pair at network level reflects
average distance
– Density (group measure)
• Robustness of the network
• Number of connections that exist in the group out of 100% possible
– Betweenness (individual measure)
• # shortest paths between each node pair that a node is on
• Measure at the individual node level
• Node roles
C
– Peripheral – below average centrality
– Central connector – above average centrality
– Broker – above average betweenness
D
E
E
D
F
A
B C
H
G
I

6. Why I Use NodeXL
Built on Excel
Easy to learn
User friendly
Flexible
FREE*
Community of users

7. Using NodeXL to Analyze
Innovation Networks

8. Using NodeXL to Analyze
Innovation Networks

9. Visualizing CrunchBase Data
Global Capital Flows Accelerators, Investors & Startups

10. Why networks & technology matter
• Startups need to be seeded into
strong clusters
• Clusters have strong connections to
markets, supply chains and talent
pools
• Clusters form around technologies
• Capital flows around technologies

11. What about social media?

12. 6 Kinds of Twitter Networks

13. Cool pictures but so what?
The network you have
The network you want

14. What Now?
Contemplate your hashtags Get started with NodeXL
Thanks to Marc Smith for letting me cannibalize his slides