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

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

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.

• 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

Frequently used metrics• Positions between communities are important –

shortest paths• Betweenness centrality, structural holes, brokerage

roles

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_Florentine_Marriges_Data_from_Padgett_and_Ansell.pdf

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’

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’

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

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

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.

Getting data into Gephi

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

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’