Social Network Analysis

Open Elective IV

Presented by : Ms. Drashti S.

 Syllabus

Lecture No. Content Duration(Hrs) Self-Study (Hrs)

1 Introduction to Semantic Web 1 1

2 The Social Web- Social Network analysis 1 1

3 Development of Social Network Analysis 1 1

4 The concepts and measures in network analysis 2 2

5 Blogs and online communities 1 1

6 Web-based networks 1 1

7 Applications of Social Network Analysis 1 1

8 Advantages and disadvantages in social networks. 1 1

Lecture 3 - Development of Social Network
Analysis

- Evolution of Social Network Analysis

- Evolution of SNA - Example
- The beginning
- The continuation process
 Evolution of SNA
• The field of Social Network Analysis today is the result of the convergence of
several streams of applied research in sociology, social psychology and
anthropology.
• Many of the concepts of network analysis have been developed independently by
various researchers often through empirical studies of various social settings.
• For example, many social psychologists of the 1940s found a formal description of
social groups useful in depicting communication channels in the group when trying
to explain processes of group communication. Already in the mid-1950s
anthropologists have found network representations useful in generalizing actual
 Evolution of SNA - Example
• Some of the concepts of network analysis have come naturally from social studies.

• In an influential early study at the Hawthorne works in Chicago, researchers from

Harvard looked at the workgroup behavior (e.g. communication, friendships, helping,
controversy) at a specific part of the factory, the bank wiring room.
• The investigators noticed that workers themselves used specific terms to describe
who is in ―our groupǁ. The researchers tried to understand how such terms arise by
reproducing in a visual way the group structure of the organization as it emerged
from the individual relationships of the factory workers.
 Evolution - the actual beginning
• Despite the various efforts, each of the early studies used a different set of concepts
and different methods of representation and analysis of social networks. However,
from the 1950s network analysis began to converge around the unique world view
that distinguishes network analysis from other approaches to sociological research.
(The term ―social network has been introduced by Barnes in 1954.)
• This convergence was facilitated by the adoption of a graph representation of social
networks usually credited to Moreno.
 Evolution - the continuation process
• What Moreno called a sociogram was a visual representation of social networks as a
set of nodes connected by directed links.
• The nodes represented individuals in Moreno‘s work, while the edges stood for
personal relations. However, similar representations can be used to depict a set of
relationships between any kind of social unit such as groups, organizations, nations
etc.
• The following decades have seen a tremendous increase in the capabilities of
network analysis mostly through new applications.
 Final plot
• SNA gains its relevance from applications and these settings in turn provide the
theories to be tested and greatly influence the development of the methods and the
interpretation of the outcomes.
• For example, one of the relatively new areas of network analysis is the analysis of
networks in entrepreneurship, an active area of research that builds and contributes to
organization and management science. The vocabulary, models and methods of
network analysis also expand continuously through applications that require to
handle ever more complex data sets.
 Lecture 3 : Takeaway
Q.1 The term ―social network has been introduced by Barnes in ___
a) 1950
b) 1954
c) 1960
d) 1990

Q.2 Define social network analysis. Explain evolution of term.

Lecture 4 - The concepts and measures in
network analysis
- What is a network?
Basic concepts :
- Actors and relationships / Nodes and edges
- Edge Direction
- Edge Weight
- Centrality Measures : Degree, closeness and betweenness
- Network Level Measures : Size and Density
 Network
• A network refers to a structure representing a group of objects/people and
relationships between them. It is also known as a graph in mathematics.
• A network structure consists of nodes and edges.
• Networks are all around us — road networks, internet networks and online social
networks like Facebook. For example, if we are studying a social relationship
between Facebook users, nodes are target users and edges are relationships such as
friendships between users or group memberships.
• In Twitter, edges can be following/follower relationships.
 Nodes and edges
• In network science, actors are referred to as
nodes (the dots on the graph) and relationships
as edges (the lines on the graph).
• Nodes can represent a variety of actors. For
example, in internet networks nodes can
represent web pages while in social networks
nodes can represent people.
• Edges can represent a variety of relationships.
In internet networks, edges can represent
hyperlinks and in social networks edges can
represent connections. Nodes and edges are a
key concept in networks, so make sure you
have a good understanding of them before
tackling the other concepts.
 Edge Direction
• There are two types of edges: directed and undirected. It will be necessary to
decipher what type of edge your data contains when building a network graph.
• Directed edges are applied from one node to another with a starting node and an
ending node. For example, when a Twitter user tags another Twitter user in a tweet,
that relationship is directed. The user who wrote the tweet (starting node) applied that
relationship to the user who they tagged (ending node). The tagged user has not
necessarily reciprocated that relationship.
• Undirected edges are the opposite of directed edges. These relationships are
reciprocated by both parties without a clear starting node or ending node. For
example, if two people are friends on Facebook, that relationship is undirected. This
is because person A is friends with person B, but we can also say person B is friends
with person A.
 Edge Weight
• An edge’s weight is the number of times that edge appears between two
specific nodes.
• For example, if person A buys a coffee from a coffee shop three times,
the edge connecting person A and the coffee shop will have a weight of
three.
• However, if person B only buys coffee from the coffee shop once, the
edge connecting person B and the coffee shop will have a weight of one.
 Centrality measures
• Centrality is a collection of metrics used to quantify how important and
influential a specific node is to the network as a whole.
• There are several centrality measures, but we will cover :
1. Degree
2. Closeness
3. Betweenness
 Centrality measure 1 : Degree
• A node’s degree is the number of edges
the node has.
• In an undirected network, there’s only
one measure for degree. For example,
if node A has edges connecting it to
node B and node D, then node A’s
degree is two.
• In a directed graph network, there is
in-degree (incoming edges) and
out-degree(outgoing edges).
 Centrality measure 2 : Closeness
• Closeness measures how well connected a
node is to every other node in the network.
• A node’s closeness is the average number
of hops required to reach every other node
in the network.
• A hop is the path of an edge from one
node to another.
• For example, node A is connected to node
B, and node B is connected to node C. For
node A to reach node C it would take two
hops.
 Centrality measure 3 : Betweennes
• Betweenness centrality is a way of detecting the
amount of influence a node has over the flow of
information in a graph.
• It is often used to find nodes that serve as a bridge
from one part of a graph to another.
• The algorithm calculates unweighted shortest paths
between all pairs of nodes in a graph. Each node
receives a score, based on the number of shortest
paths that pass through the node.
• Nodes that more frequently lie on shortest paths
between other nodes will have higher betweenness
centrality scores.
 Network Level Measures
• We can also calculate metrics on the network level to evaluate the entire
network instead of merely a single node. Like centrality measures, there
are a variety of network-level measures.
• There are two measures we will look:
1. Network size
2. Density
 Network Level Measures
1. Network size: 2. Network Density:
Network size is the number of nodes in Network density is the number of edges
the network. The size of a network does divided by the total possible edges. For
not take into consideration the number of example, a network with node A
edges. For example, a network with nodes connected to node B, and node B
A, B, and C has a size of 3. connected to node C, the network density
is 2/3 because there are two edges out of a
possible three.
 Lecture 4 : Takeaway
Q.1 What are the various Centrality measures of a network ?
Q.2 Explain the various Network measures of a network?
Q.3 Discuss the various parameters of a network graph.