Social Network Analysis

Examples of Social Network Analysis in the following topics:

• Preface

  • This book began as a set of reading notes as Hanneman sought to teach himself the basics of social network analysis.
  • It then became a set of lecture notes for students in his undergraduate course in social network analysis.
  • Our goal in preparing this book is to provide a very basic introduction to the core ideas of social network analysis, and how these ideas are implemented in the methodologies that many social network analysts use.
  • Social network analysis is a continuously and rapidly evolving field, and is one branch of the broader study of networks and complex systems.
  • The concepts and techniques of social network analysis are informed by, and inform the evolution of these broader fields.

• After word

  • We hope that you've found this introduction to the concepts and methods of social network analysis to be of both interest and utility.
  • The basic methods of studying patterns of social relations that have been developed in the field of social network analysis provide ways of rigorously approaching many classic problems in the social sciences.
  • Social network analysis is also increasingly connected to the broader field of network analysis.
  • Hopefully, the core ideas of social network analysis will enrich our understanding of fields outside the social sciences.
  • We've not provided a rigorous grounding of social network analysis in graph theory.

• Introduction: Applying statistical tools to network data

  • Network analysis in the social sciences developed from a conjuncture of anthropologist's observations about relations in face-to-face groups and mathematical graph theory.
  • Most of the tools of social network analysis involve the use of mathematical functions to describe networks and their sub-structures.
  • Inferential statistics have also proven to have very useful applications to social network analysis.
  • In this chapter we will look at some of the ways in which quite basic statistical tools have been applied in social network analysis.
  • The development of more powerful statistical tools especially tuned for the needs of social network analysis is one of the most rapidly developing "cutting edges" of the field.

• Uses of the concept

  • Most approaches to social positions define them relationally.
  • The regular equivalence approach is important because it provides a method for identifying "roles" from the patterns of ties present in a network.
  • Rather than relying on attributes of actors to define social roles and to understand how social roles give rise to patterns of interaction, regular equivalence analysis seeks to identify social roles by identifying regularities in the patterns of network ties -- whether or not the occupants of the roles have names for their positions.
  • Regular equivalence analysis of a network then can be used to locate and define the nature of roles by their patterns of ties.
  • The identification and definition of "roles" by the regular equivalence analysis of network data is possibly the most important intellectual development of social network analysis.

• Social Networks

  • A social network is a social structure that exists between actors—individuals or organizations.
  • Facebook is an example of a large social network.
  • Social networks are composed of nodes and ties.
  • The study of social networks is called either social network analysis or social network theory.
  • Assess the role of social networks in the socialization of people

• Summary

  • In this chapter we've taken a look at some of the most basic and common approaches to applying statistical analysis to the attributes of actors embedded in networks, the relations among these actors, and the similarities between multiple relational networks connecting the same actors.
  • But, there is still a good bit more, as the application of statistical modeling to network data is one of the "leading edges" of the field of social (and other) network analyses.
  • First, for very large networks, methods for finding and describing the distributions of network features provide important tools for understanding the likely patterns of behavior of the whole network and the actors embedded in it.
  • Taken together, the marriage of statistics and mathematics in social network analysis has already produced some very useful ways of looking at patterns of social relations.
  • It is likely that this interface will be one of the areas of most rapid development in the field of social network methods in the coming years.

• Modality and levels of analysis

  • Often these networks of interpersonal relations become "social facts" and take on a life of their own.
  • Most social network analysts think of individual persons as being embedded in networks that are embedded in networks that are embedded in networks.
  • Of course, this kind of view of the nature of social structures is not unique to social network analyst.
  • One advantage of network thinking and method is that it naturally predisposes the analyst to focus on multiple levels of analysis simultaneously.
  • Having claimed that social network methods are particularly well suited for dealing with multiple levels of analysis and multi-modal data structures, it must immediately be admitted that social network analysis rarely actually takes much advantage.

• Bibliography

  • This page lists a number of early works in social network analysis.
  • "Graph theory in network analysis" Social Networks 5: 235-244.
  • Social structure and network analysis.
  • Social Network Analysis: A Handbook.
  • Social Network Analysis: Methods and Applications.

• Introduction

  • The basic idea of a social network is very simple.
  • A social network is a set of actors (or points, or nodes, or agents) that may have relationships (or edges, or ties) with one another.
  • To build a useful understanding of a social network, a complete and rigorous description of a pattern of social relationships is a necessary starting point for analysis.
  • The amount of information that we need to describe even small social networks can be quite great.
  • All of the tasks of social network methods are made easier by using tools from mathematics.

• Summary

  • They are often used in network analysis to represent the adjacency of each actor to each other actor in a network.
  • The main diagonal, or "self-tie" of an adjacency matrix is often ignored in network analysis.
  • Vector operations, blocking and partitioning, and matrix mathematics (inverses, transposes, addition, subtraction, multiplication and Boolean multiplication), are mathematical operations that are sometimes helpful to let us see certain things about the patterns of ties in social networks.
  • Social network data are often multiplex (i.e. there are multiple kinds of ties among the actors).
  • In the remainder of the book, we will look at how social network analysts have formally translated some of the core concepts that social scientists use to describe social structures.