Introduction to Social Networks: Networks Structures and Information Systems

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Lecture 9

• Introduction to Social Networks Networks
  Structures and Information Systems

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What is Social Network Analysis?

• Network analysis is the study of social relations
  among a set of actors. It is a field of study,
  not just a method.
• Social network analysis involves theorizing,
  model building and empirical research focused on
  uncovering the patterning of links among actors.
  It is concerned also with uncovering the
  antecedents and consequences of recurrent
  patterns. (Linton Freeman)

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The network perspective
People (nodes)
Ties (edges)
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Ties in a social network

• Directed or undirected
• Simplex or multiplex
• Valued or unvalued

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What is a Social Network?

• A set of dyadic ties, all of the same type, among
  a set of actors
• Actors can be persons, organizations, groups
• A tie is an instance of a specific social
  relationship

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Network Relations

• Among Individuals
• Kinship
• Role-based (friend of)
• Cognitive/Perceptual (knows, aware of)
• Affiliations
• Affective (likes, trusts)
• Communication
• Among Organizations
• Buy from / Sell to
• Owns shares of
• Joint ventures

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Key Perspectives in Social Network Analysis

• Focus on relationships between actors rather than
  just the attributes of actors.
• Interdependent view rather than atomistic
  (individualist) view of social processes and
  effects.
• Social structure affects substantive outcomes
  (which is a philosophical departure from other
  traditions)

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Interdisciplinary Field of Study

• Computer Science
• Designing and understanding complex network
  structures
• Mathematics, Physics
• Methods, complex systems analysis
• Social Science (Sociology, Social Psychology,
  Economics)
• Theories and measurement of social networks,
  using networks to understand human behavior

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Multiple Levels of Analysis

• Individual Level
• How does individual position in a network affect
  various outcomes for the individual?
• Systems Level
• How does the network structure as a whole affect
  outcomes for various tasks?

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Network Data Collection

• Common Types
• Survey
• Interviews
• Affiliation/membership records
• Behavioral (e.g., observation of communication
  patterns)
• Experiments

Data obtained through manyeyes and graphed
http//www.esv.org/blog/2007/01/mapping.nt.social.
networks
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Types of Network Data

• One mode Two mode
• Whole network Egocentric

A
B
A
B
C
School A
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Non-directed versus Directed Graphs
A
B
A
B
C
C
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Analyzing Social Networks
A
D
B
C
Simple Adjacency Matrix
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Some Key Principles in Social Networks

• Degree
• Density
• Centrality

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Degree in Social Networks
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Density in Social Networks
Low Density
High Density / Integrated
Radial (Valente)
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Centrality in Social Networks

• Degree Centrality
• Closeness Centrality
• Betweeness Centrality

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Why all of this sudden interest?

• The strength of the Strength of Weak Ties
  argument.
• Granovetter (1973)
• Argues that weaker peripheral ties build
  heterogeneous networks, which in turn provide
  access to new and useful information.
• Heterogeneity through weak-ties widely accepted
  as a good thing for communication
• Access to jobs
• Access to other opportunities
• Helps distribute ideas, innovations