Social networks are based on relations between two or a few individuals from friendships over contracts to work contacts.
Throughout the course, the theory behind social networks will be put into context with methods of comparing and applying social networks. Examples from different scientific disciplines will be used to illustrate the social networks.
Mathematical descriptions of networks are a useful descriptive. An adjacency matrix can be used to represent a graph as nodes and edges.
Networks can be analysed on different levels:
- Dyad level () or connections between nodes
- Node level () or properties of nodes
- Network level () or clustering of nodes.
Centrality could be access to resources, connection between parts, part of interaction. For a detailed report on centrality measures, look at this post in my Complexity and Global Systems Sciences lecture notes. Centrality measures often differ and in larger networks will be different for different measures. The choice of centrality is dependent on the research question.
Generally, for any network, one should start with the following descriptives, before continuing to more advanced analysis.
- Start with a visualisation of a network.
- Compute density of network (number of edges divided by maximal number of edges; note that the maximal number is different for directed ( ) and undirected () graphs).
- Measure centrality in social networks.