Very brief introduction to SNA

1. ‘Social network analysis 101’
(the concepts, without the maths)

3. What is it?
• Social network analysis is a toolkit of approaches built on the
fundamental idea that a social relationship between two
people can be conceptualised as a link (‘edge’ or ‘tie’)
between two people (‘nodes’, ‘vertices’ or ‘actors’)
• Depending on the relationship, this can be directed or
undirected
• One mode or two mode networks
• Advantages of being able to visualise previously obscure
relationships, and use graph theory to model processes
Node NodeNode

5. Frequently used metrics
• Network size: degree
• If directed, this can be considered in terms of in-degree and
out-degree
• Typically follows a power law distribution
Albert-Laszlo Barabasi, Linked: The
New Science of Networks.

6. • But how connected are the nodes within a
network?
• Density = proportion of possible connections
which do exist
• A clique = a set of nodes in which all
possible connections exist
• Smallest clique = a triad
• Clustering coefficient, community detection
methods
Frequently used metrics

7. Frequently used metrics
• Positions between communities are important – shortest paths
• Betweenness centrality, structural holes, brokerage roles

8. Origins
• Origins date back to early
20th century Sociology
• “[SNA] itself is neither
quantitative nor qualitative,
nor a combination of the
two. Rather, it is structural”
(Carrington, 2014, p.35)
• Interpretation of networks
depends on goals and
epistemology of studies
Image source: Bbuuggzz
https://en.wikipedia.org/wiki/File:15th_Century_Flore
ntine_Marriges_Data_from_Padgett_and_Ansell.pdf

9. Classic studies: Milgram’s small
world
• Sought to determine the average path length between two
nodes in a population
• Randomly selected people in Nebraska and Kansas
• Had to forward information to someone they knew personally,
with the goal of it reaching a target contact in Boston,
Massachusetts.
• 64 of 296 letters reached destination
• Hops ranged from 1 to 10; average number was six
• Origin of the phrase ‘six degrees of separation’

10. Classic studies: Granovetter’s
jobseekers
• First published in 1973
• Interviewed 100 people to find out how they used their social
networks to get new jobs
• ‘Strong ties’ are close friends, highly connected to ego and
often each other; ‘weak ties’ are less frequently met,
acquaintances
• Acquaintances more frequently the source of information
leading to new jobs; weak ties more likely to provide novel
information
• ‘The strength of weak ties’

11. Classic studies: Burt’s
brokerage
• Elaborated on links between structural characteristics of networks
and links to social capital
• Social capital: “networks together with shared norms, values and
understandings that facilitate co-operation within or among
groups” (OECD definition)
• ‘Structural holes’ as gaps between communities which could be
usefully exploited
• ‘Brokers’ as key nodes which mediate flow of information between
otherwise unconnected communities
• Nodes which are positioned between different communities can
have advantages and disadvantages in terms of social capital

12. SNA in the era of Big Data
• Networks everywhere?
• But how valid are the links?
• Automated network extraction does not account for
context.
• Unlike genes or hyperlinks, people have agency.
• E.g. are all your Facebook friends equally important to you?
• -> Importance of mixed methods to validate understanding

13. Some considerations
• Which level of network to focus on?
• Directed or undirected?
• One-mode or two-mode?
• Can learn from small networks too.
• If using statistical tests, bear in mind that many metrics don’t
follow a normal distribution (e.g. power laws).
• How relationships (edges) are defined, and how
confident you can be in the accuracy of what they
represent, is essential.

14. Getting data into Gephi

15. Benefits of using Gephi
• It’s free
• Works on both PCs and Macs
• Various plugins are available – e.g. export as web pages, fix
nodes to geographical co-ordinates
• Active community for support online
• Relatively user friendly
• Attractive visualisations
• Can export in various formats to other packages - .gexf or
.gml as a good lingua franca

16. What Gephi needs
• An edges table
• A nodes table (optional)
• You can enter this manually, or import data as .csv files
• An edges table is a .csv file with two columns: ‘source’ and
‘target’