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Social Media Analytics Meetup
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
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