With our lives moving towards the online space through social media, the ways that we can connect with others is nearly limitless. There are two types of connections that can be made in social media: explicit and implicit. Explicit connections are made through actions such as following, friending, and tagging, whereas implicit connections are made through actions such as commenting, poking, and messaging. If you consider each person on a network to be a dot, and each connection they make acting as a way to connect the dots, you will soon see a web connecting people, locations, and the objects around them together – and this is your social network.


Meet the social network!

The creation of these networks provides us with another source of data to be analysed for interesting insights in regards to networks. Social network analysis (SNA) refers to the measuring and mapping of the relationships between people, organisations, groups, and other connected entities. Individual actors (e.g. people, animals, things, collectives) are represented as nodes, and relationships (e.g. kinship, social, cognitive, interaction) between the actors are known as ties. As there are many different kinds of ties between nodes, it allows us to investigate and answer several interesting questions about networks:

  • What is the role of the individual in a social network?
  • How do ideas spread throughout a network, and who influences these?
  • What kind of subgroups exists – are there any connections between these, and how are these connections made?
  • How can events influence the structure of a social network?

There are also several key network measures to consider when using SNA:

The final element that needs to be taken into consideration for SNA are centrality measures, which help to identify the most important vertices in a graph, usually through the use of algorithms – ‘which nodes are more central than others’. The key centrality measures are:

  1. Degree – measures the activity of an actor through the number of direct ties that they have e.g. how many people has an artist directly worked with? It is the simplest of the centrality measures.
  2. Closeness – measures how quickly actors can reach other nodes i.e. the average geodesic distance between actors e.g. how fast can a rumour spread around a school?
  3. Betweeness – measures the extent to which an actor benefits from being the closest to other actors e.g. the person who connects two separate parties together and has control of information
  4. Eigenvector – measures influence that accounts for the numbers of links each actor has, and the number of links their connections have e.g. people who are connected to influential actors, as opposed to those that would be ‘low-scoring’

This is only a very brief overview of the key elements involved in social network analysis – for some clarity on how it can be used, take a look at a case study in the next post!

Has this post helped you with understanding the basis of Social Network Analysis? Is there anything else that you would like to see or think could be made clearer?