This document provides an overview of social network analysis (SNA). SNA is not just a methodology but a unique perspective that focuses on relationships between individuals, groups, and institutions rather than individuals alone or macro social structures. Practical applications of SNA include improving communication in organizations, identifying criminal networks, and recommending friends in social networks. The document outlines key SNA concepts like representing networks as graphs, identifying strong/weak ties, central nodes, and measures of network structure such as cohesion, density, and clustering.

1. Social Network Analysis
(SNA)

2. Background
• Network analysis concerns itself with the
formulation and solution of problems that
have a network structure; such structure is
usually captured in a graph.
• Graph theory provides a set of abstract
concepts and methods for the analysis of
graphs.
• Social Science

3. SNA
•SNA is not just a methodology; it is a unique
perspective on how society functions.
•Instead of focusing on individuals and their
attributes, or on macroscopic social structures, it
centers on relations between individuals,
groups, or social institutions.

4. Practical Applications
• Businesses
– improve communication flow in the organization
• Law enforcement
– identify criminal and terrorist networks
– key players in these network
• Social Network
– identify and recommend potential friends based on
friends-of-friends
• Network operators (telephony, cable, mobile)
– optimize the structure and capacity of their networks

5. Why and When to use SNA
• Unlimited possibilities
• To improve the effectiveness of the network to
visualize your data so as to uncover patterns
in relationships or interactions
• To follow the paths that information follows in
social networks
• Quantitative research & qualitative research

6. Basic Concepts
• How to represent various
social networksNetworks
• How to identify strong/weak
ties in the networkTie Strength
• How to identify key/central
nodes in networkKey Players
• Measures of overall network
structureCohesion

7. • How to represent various
social networksNetworks

8. Representing relations as networks
Communication
• Anne: Jim, tell the Murrays they’re invited
• Jim: Mary, you and your dad should come for dinner!
• Jim: Mr. Murray, you should both come for dinner
• Anne: Mary, did Jim tell you about the dinner? You must come.
• John: Mary, are you hungry?
1 2
3 4
Vertex
(node)
Edge
(link)
Graph1 432

9. Directed Graph

10. Undirected Graph

11. Ego and Whole Network

12. • How to identify strong/weak
ties in the networkTie Strength

13. Weights to the Edges
(Directed/Undirected Graphs)

14. Homophily, Transitivity, Bridging
Homophily
• Tendency to relate to people with
similar characteristics (status, beliefs, etc.)
• Ties can either be strong or weak.
• Leads to formation of clusters.
Transitivity
• Transitivity is evidence to Existence of strong ties.
• Transitivity and homophily lead to formation of
cliques.
Bridging
• They are nodes and edges connected across groups.

15. • How to identify key/central
nodes in networkKey Players

16. Degree Centrality

17. Betweenness Centrality

18. Closeness Centrality

19. Eigenvector Centrality

20. Paths and Shortest Path

21. Interpretation of Measures(1)

22. Interpretation of Measures(2)

23. Identifying set of Key Players

24. • Measures of overall network
structureCohesion

25. Reciprocity (degree of)
• The ratio of the number of relations
which are reciprocated (i.e. there is an edge
in both directions) over the total number of
relations in the network.
• where two vertices are said to be related
if there is at least one edge between them
• In the example to the right this would be
2/5=0.4 (whether this is considered high or
low depends on the context)

26. Density
• A network’s density is the ratio of the
number of edges in the network over
the total number of possible edges
between all pairs of nodes (which is n(n-1)/2,
where n is the number of vertices, for an
undirected graph)
• In the example network to the
right density=5/6=0.83

27. Clustering
• A node’s clustering coefficient is the
density of its neighborhood (i.e. the
network consisting only of this node
and all other nodes directly connected
to it)
• E.g., node 1 to the right has a value
Of 1 because its neighbors are 2 and 3
and the neighborhood of nodes 1, 2
and 3 is perfectly connected (i.e. it is a
‘clique’)

28. Average and Longest Distance
• The longest shortest path (distance)
between any two nodes in a network
is called the network’s diameter
• The diameter of the network on
the right is 3

29. Small world
• A small world is a network that
looks almost random but exhibits a
significantly high clustering coefficient
(nodes tend to cluster locally) and a
relatively short average path length
(nodes can be reached in a few steps)

30. Preferential Attachment

32. THANK YOU