Keep it simple:

1. Select a limited
group of actors to research. Be sure what the limit for participation is. If
you start investigating the networks of too many, you will never finish.

2. Who are actors – what
is a relation. Define which types of relations you want to include. Do family
ties count? High school friends? Drinking buddies? Often we only have formal
ties accessible. If we want to use informal ties a lot of research will usually
be needed. But formal ties are fine. First of all it’s easier to research;
secondly it’s harder to deny these ties.

3. Get to know a program
that can analyze social networks, for example UCINET. Try doing different
calculations and see what happens. Just for fun.

4. Put the data into the
program. Don’t despair when you see the visual result of your data as a
hairball. Try excluding the unimportant actors to make the hairball less dense.
Unimportant actors can be people who only have one single tie into the network.

5. Have the program do
the simple calculations of degree centrality, closeness and betweenness. Do the
story based on these results.

6. Illustrate the story
with the networks of the most central persons, not the whole network (nobody
can conclude anything from the hairball)


Degree centrality: Shows how many other
people in the network an individual knows. The higher the number, the more
central a person is. A person with a high degree centrality is theoretically
“where it happens” in a network.


Closeness: Shows how fast a person
can get into contact with the whole network. A high score here indicates that a
person is in a good position to monitor the flow of information in the network.


Betweenness: Shows to what degree a
person is placed as a gatekeeper between two subgroups in the network. A high
betweenness gives a person a great power over what flows through the network.



more info here:

Reporters and Editors webpage. You’ll find lots of info on SNA. Links, stories,
tipsheets etc.

on-line textbook introduces many of the basics of formal approaches to the
analysis of social networks. It may be the best starting point.