This document discusses social network analysis (SNA) and provides an example analysis of the author's personal network on LinkedIn. SNA is a tool that represents individuals as nodes and maps their relationships. It provides insight into roles, groups, and power structures. The author's LinkedIn network map shows different cliques and relationships between them. Analysis of the map reveals information about the author's central role and the positioning of others, such as peripheral alumni. However, limitations include assumptions made in LinkedIn's methodology.

1. Social Network Analysis
Rachel Jones
MA Corporate Communication
July 2012

2. About Social Network Analysis (SNA)
• Social Network Analysis (SNA) is an effective tool for
investigating relations in an active community of
persons.
• SNA gives insight into the various roles, groupings
and power structures within the network.
• Based upon mathematical formulae developed by
Freeman 1979, Bonacich 1987 and applied and
developed to networks by Burt 1992, Hanneman
2000, Scott 2010. (Jimoyiannis and Angelaina 2012)

3. Elements of SNA
• Within the network, individuals are represented as nodes.
• Degree Centrality is the number of direct connections to a node.
An active node can be known as a ‘connector’ or ‘hub’.
• Degree Centrality is not a measure of influence, some connectors
will only have links to those in their immediate clique or group and
not outside of the cluster.
• Betweenness Centrality can indicate brokers in the network
between constituencies.
• Closeness centrality are the nodes with the shortest paths to
others and are positioned to monitor information flow in the
network.
• Peripheral nodes are those with the smallest number of connecting
lines on the outside of cliques.
• Structurally equivalent nodes are symmetrically placed within the
clique. (Social Network Analysis, A Brief Introduction 2012)

4. My LinkedIn network
Using LinkedIn Maps to visualise my professional
network.
• University of Gloucestershire
• BA Hons PR students Uni of Gloucestershire
• Gloucestershire PR community
• MA Communications Sheffield Hallam
• BBC
• Manchester University
• UCAS
• Other/Media

6. Reading the map
• The colours show different cliques, and show the relationships
between different cliques and individuals (nodes.)
• The signed-in version shows named contacts which helps to
identify key individuals.
• I am central to my map so all connections centre upon my
node.
• The larger the node, the more connections.
• Social patterns emerge from the map to show how cliques
interact or are separate.
• Few connections between the ‘University of Gloucestershire’
clique in blue and the ‘University of Gloucestershire BA PR
course’ in orange.

7. Snapshot of one clique
BA Hons PR course at the
University of Gloucestershire
Yellow = Tutor
Orange = Students/alumni

8. Analysis
• Map shows that all the students I am connected with are also
connected to each other.
• Tutors are also connected individually to most students, but not
necessarily each other. They are peripheral to the student clique.
• There are no truly structurally equivalent nodes using the LinkedIn
software however the right-hand cluster of students which suggests
similar scope of reach and influence within ‘my’ map.
• Four left-hand nodes are notably peripheral. They represent course
alumni and outliers who are connected to the clique professionally
eg they are journalists.
• The lecturers demonstrate betweeness centrality between external
contacts and the students. There is also a student on the bottom
left who occupies this position. This student has an award-winning
blog and has much social media expertise and his positioning
represents his network reach beyond the clique.

9. Benefits and limitations
• Provides easy snapshot of different cliques and
any links between them.
• When nodes are named ie individuals identified it
helps to understand relationships eg degree
centrality.
• Does not follow the textbook example of a SNA
so not easy to interpret connections.
• LinkedIn do not provide methodology for
development of the map, so assumptions are
made that they follow SNA mathematic formula
and that degrees of closeness are true.

10. Bibliography
Jimoyiannis A and Angelaina S (2012) Towards an
analysis framework for investigating
students' engagement and learning in educational
blogs. Journal of Computer Assisted
Learning. 28, 222-234.
Social Network Analysis, A Brief Introduction
http://orgnet.com/sna.html accessed 31 July 2012