networking

(noun)

the act of meeting new people in a business or social context.

Related Terms

Examples of networking in the following topics:

  • Social Networks

    • Facebook is an example of a large social network.
    • Social networks are composed of nodes and ties.
    • Smaller, tighter networks composed of strong ties behave differently than larger, looser networks of weak 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

  • Modality and levels of analysis

    • The network analyst tends to see individual people nested within networks of face-to-face relations with other persons.
    • Often these networks of interpersonal relations become "social facts" and take on a life of their own.
    • A family, for example, is a network of close relations among a set of people.
    • Most social network analysts think of individual persons as being embedded in networks that are embedded in networks that are embedded in networks.
    • In chapter 17, we'll take a look at some methods for multi-mode networks.

  • 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.
    • Second, we have increasingly come to realize that the relations we see among actors in a network at a point in time are best seen as probabilistic ("stochastic") outcomes of underlying processes of evolution of networks, and probabilistic actions of actors embedded in those networks.
    • And, we've taken a look at a variety of approaches that relate attributes of actors to their positions in networks.

  • Introduction

    • The basic idea of a social network is very simple.
    • Networks can have few or many actors, and one or more kinds of relations between pairs of actors.
    • 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.
    • For the manipulation of network data, and the calculation of indexes describing networks, it is most useful to record information as matrices.

  • Networks

    • The study of social networks is called both social network analysis and social network theory.
    • Social network theory views social relationships in terms of nodes and ties.
    • The shape of the social network helps determine a network's usefulness to its individuals.
    • Smaller, tighter networks can be less useful to their members than networks with lots of loose connections (weak ties) to individuals outside the main network.
    • It is better for individual success to have connections to a variety of networks rather than many connections within a single network.

  • 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.

  • Introduction: What's different about social network data?

    • On one hand, there really isn't anything about social network data that is all that unusual.
    • "Network" data (in their purest form) consist of a square array of measurements.
    • This is the first major emphasis of network analysis: seeing how actors are located or "embedded" in the overall network.But a network analyst is also likely to look at the data structure in a second way -- holistically.
    • Indeed, many of the techniques used by network analysts (like calculating correlations and distances) are applied exactly the same way to network data as they would be to conventional data.While it is possible to describe network data as just a special form of conventional data (and it is), network analysts look at the data in some rather fundamentally different ways.
    • But the special purposes and emphases of network research do call for some different considerations.In this chapter, we will take a look at some of the issues that arise in design, sampling, and measurement for social network analysis.

  • Introduction: Applying statistical tools to network data

    • A very large part of social network methodology, consequently, deals with relatively small networks, networks where we have confidence in the reliability of our observations about the relations among the actors.
    • Most of the tools of social network analysis involve the use of mathematical functions to describe networks and their sub-structures.
    • Increasingly, the social networks that are being studied may contain many nodes; and, sometimes our observations about these very large networks are based not on censuses, but on samples of nodes.
    • All of these concerns (large networks, sampling, concern about the reliability of observations) have led social network researchers to begin to apply the techniques of descriptive and inferential statistics in their work.
    • Inferential statistics have also proven to have very useful applications to social network analysis.

  • A note on statistics and social network data

    • Social network analysis is more a branch of "mathematical" sociology than of "statistical or quantitative analysis," though social network analysts most certainly practice both approaches.
    • Statistical analysts also tend to think of a particular set of network data as a "sample" of a larger class or population of such networks or network elements -- and have a concern for the results of the current study would be reproduced in the "next" study of similar samples.
    • Algorithms from statistics are commonly used to describe characteristics of individual observations (e.g. the median tie strength of actor X with all other actors in the network) and the network as a whole (e.g. the mean of all tie strengths among all actors in the network).
    • In many cases, they are studying a particular network or set of networks, and have no interest in generalizing to a larger population of such networks (either because there isn't any such population, or we don't care about generalizing to it in any probabilistic way).
    • Suppose, for example, that I was interested in the proportion of the actors in a network who were members of cliques (or any other network statistic or parameter).

  • Bibliography

    • "Graph theory in network analysis" Social Networks 5: 235-244.
    • Social structures: A network approach.
    • A graph theoretic blocking procedure for social networks, Social Networks, 4: 147-167
    • Centrality in social networks: Conceptual clarification, Social Networks, 1: 215-39.
    • The Urban Black Community as Network: Toward a Social Network Perspective.