Basics of Social Network Analysis
In this video Dr Nigel Williams explores the basics of Social Network Analysis (SNA): Why and how SNA can be used in Events Management Research.

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Why Social Network Analysis (SNA)?
Enables us to segment data based on user behavior.
Understand natural groups that have formed:
a. topics
b. personal characteristics
Understand who are the important people in these groups.


Analysing Social Networks:
Data Collection Methods:
a. Surveys
b. Interviews
c. Observations
Analysis:
a. Computational analysis of matrices

Relationships:
A. is connected to B.

SNA Introduction:
[from] A. Directed Graph [to] B. e.g. Twitter replies and mentions
A. Undirected Graph B. e.g. family relationships

What is Social Network Analysis?
Research technique that analyses the Social structure that emerges from the combination of relationships among members of a given population (Hampton & Wellman (1999); Paolillo (2001); Wellman (2001)).

Social Network Analysis Basics: Node and Edge
Node:
“actor” or people on which relationships act

Edge:
relationship connecting nodes; can be directional

Social Network Analysis Basics: Cohesive Sub-group
Cohesive Sub-group:
a. well-connected group, clique, or cluster, e.g. A, B, D, and E

Social Network Analysis Basics: Key Metrics
Centrality (group or individual measure):
a. Number of direct connections that individuals have with others in the group (usually look at incoming connections only).
b. Measure at the individual node or group level.

Cohesion (group measure):
a. Ease with which a network can connect.
b. Aggregate measure of shortest path between each node pair at network level reflects average distance.

Density (group measure):
a. Robustness of the network.
b. Number of connections that exist in the group out of 100% possible.
Betweenness (individual measure):
a. Shortest paths between each node pair that a node is on.
b. Measure at the individual node level.

Social Network Analysis Basics: Node Roles:
Node Roles:
Peripheral – below average centrality, e.g. C.
Central connector – above average centrality, e.g. D.
Broker – above average betweenness, e.g. E.

References and Reading
Hampton, K. N., and Wellman, B. (1999). Netville Online and Offline Observing and Surveying a Wired Suburb. American Behavioral Scientist, 43(3), pp. 475-492.

Smith, M. A. (2014, May). Identifying and shifting social media network patterns with NodeXL. In Collaboration Technologies and Systems (CTS), 2014 International Conference on IEEE, pp. 3-8.

Smith, M., Rainie, L., Shneiderman, B., and Himelboim, I. (2014). Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. Pew Research Internet Project.