Social Network Analysis #1

Imagine a human pyramid, one of those Circus acts with everyone on each others shoulders tottering rather precariously.  Is that structure or agency?  Is it a network, is it a diagram.  Well maybe its social network analysis: below we’ll try to set out the basics.  Of course it comes in many forms and there are many uses, but this is just a rough guide.

Social Network Analysis

For the basics on the development of social network analysis, tracing its origins in classical sociology and its more recent formulation in social scientific and mathematical work see the work of John Scott. He argued that the concept of social network provides a powerful model for social structure, and offers a number of important formal methods of social network analysis. Scott also notes that ocial network analysis has been used in studies of kinship structure (yet not used by Young & Wilmott or Margaret Mead!), social mobility, science citations, contacts among members of deviant groups, corporate power, international trade exploitation, class structure, and many other areas. So we’ll try to simplify his ideas below and go into a bit more detail in the second part of this.


Representations of networks as graphs, sets and matrices

John Scott, Social Network Analysis

Wasserman and Faust


Types of Networks are:

(1) Ego-Centered Networks

(2) One-Mode Networks

(3) Two-Mode (Affiliation) Networks


From Structure to Networks:

We can deal with the complexity of networks by grouping the alters into structurally equivalent blocks, and considering all ties the same.

Hence You are tied to your Aunt through a “sister” tie appended to a “mother” or “father” tie.

While networks are often touted as an alternative perspective to classic role theory, really the most interesting aspects have been an attempt to re-create role theory.

(1) The Idea of Social Structure

(2) Kinship Structures (Claude Levi-Strauss, The Elementary Structures of Kinship)

(3) Structure from Networks

(4) A New Network Anthropology


Properties of Nodes:

(1) Attractiveness and Expansiveness: popularity and niceness

(2) Centrality

(3) Embededdeness and Social capital

(4) Brokerage

(5) Power


Properties of Networks:

Especially important in communication and epidemiological networks

(1) Reaching many people or avoiding some (Connectedness and Span)

(2) Balance (especially important in friendship networks)

(3) Transitivity (important in many networks and organizations)

(4) Trees (important in organizations)

(5) Structural Equivalence


Effects of Networks:

A. Diffusion

(1) Diseases

(2) Ideas and Attitudes

B. Mutual Support

(1) Structure of, Effects of

(2) Change in and production of

C. Economic relations

(1) Exchange and competition

(2) Information and Advice

(3) Ownership


Action within and building networks:

Friendship and Acquaintance: Making Random Graphs and Small Worlds Especially importance for acquaintance and communication networks:

(1) How do people make networks?

(2) The Idea of Small Worlds

(3) The Creation of Small Worlds—Acquaintance and Friendship

(4) Problems on Small Worlds – the map is not the territory



(1) Strategic Tie Construction (especially important for business networks)

(2) Retention and Destruction of Ties (especially important for friendship networks)


Acting Within Networks

(1) Activating Ties

(2) Mobilizing Ties (especially important for social movements)


Theories Of Networks:

Georg Simmel, Web of Affiliation (and brief  commentary)


Networks and Groups

(1) The duality of Persons and Groups

(2) From Network to Culture


Networks and Identities

(1) Networks and Language (shibboleths)

(2) Networks, Personality and Control



The design of network data has to do with what Ties or Relations are to be measured for the selected Nodes.

Given a set of actors or nodes, there are several strategies for deciding how to go about collecting measurements on the relations among them. At one end of the spectrum of approaches are “full network” methods. At the other end of the spectrum are methods that look quite like those used in conventional survey research. There is no one “right” method for all research questions and problems.

Full network methods require that we collect information about each actor’s ties with all other actors: a census of ties in a population of actors — rather than a sample. Full network data is necessary to properly define and measure many of the structural concepts of network analysis (e.g. between-ness).

Snowball methods begin with a focal actor or set of actors. Each of these actors is asked to name some or all of their ties to other actors. Then, all the actors named (who were not part of the original list) are tracked down and asked for some or all of their ties. The process continues until no new actors are identified. The snowball method can be particularly helpful for tracking down “special” populations (often numerically small sub-sets of people mixed in with large numbers of others).

There are two major potential limitations and weaknesses of snowball methods. First, actors who are not connected (i.e. “isolates”) are not located by this method. The snowball method may tend to overstate the “connectedness” and “solidarity” of populations of actors. Second, there is no guaranteed way of finding all of the connected individuals in the population.

Problems with Network Analysis

There is now an increasing interest in SNA and the most obvious explanation for this is the inter-connectedness of computers and the rise of social networking website.  Network analysis is criticized for being too much methodological and too little theoretical. Critics say that there are few truly network theories of substantive phenomena: but when examples of network theories are presented, also say ‘that’s not really a network theory’. This is natural because theories that account for, say, psychological phenomena, tend to have a lot of psychological content. Theories that account for sociological phenomena have sociological independent variables. Only theories that explain network phenomena tend to have a lot of network content.

A real problem with network analysis in the past has been the inability to test hypotheses statistically, because the data are by their very nature auto-correlated, violating assumptions of independence (random sampling) built-in to most classical statistical tests. With the advent of permutation tests, however, this is much less of problem now (chi-test).

It is often a problem to bound a social network. If we are looking at needle-sharing among drug users, we can artificially bound the network at some arbitrary boundary, such as city or neighborhood, but this distorts the data. Yet we cannot let the network get too large because we cannot process the data.


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