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Social Network Analysis Rachel JonesMA Corporate Communication July 2012
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)
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)
My LinkedIn networkUsing LinkedIn Maps to visualise my professionalnetwork.• University of Gloucestershire• BA Hons PR students Uni of Gloucestershire• Gloucestershire PR community• MA Communications Sheffield Hallam• BBC• Manchester University• UCAS• Other/Media
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.
Snapshot of one clique BA Hons PR course at the University of GloucestershireYellow = TutorOrange = Students/alumni
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.
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.
BibliographyJimoyiannis A and Angelaina S (2012) Towards ananalysis framework for investigatingstudents engagement and learning in educationalblogs. Journal of Computer AssistedLearning. 28, 222-234.Social Network Analysis, A Brief Introductionhttp://orgnet.com/sna.html accessed 31 July 2012