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4.1: Introduction - A picture is worth...

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    7665
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    As we saw in chapter 3, a graph representing the information about the relations among nodes can be an very efficient way of describing a social structure. A good drawing of a graph can immediately suggest some of the most important features of overall network structure. Are all the nodes connected? Are there many or few ties among the actors? Are there sub-groups or local "clusters" of actors that are tied to one another, but not to other groups? Are there some actors with many ties, and some with few?

    A good drawing can also help us to better understand how a particular "ego" (node) is "embedded" (connected to) its "neighborhood" (the actors that are connected to ego, and their connections to one another) and to the larger graph (is "ego" an "isolate" a "pendant"?). By looking at "ego" and the "ego network" (i.e. "neighborhood"), we can get a sense of the structural constraints and opportunities that an actor faces; we may be better able to understand the role that an actor plays in a social structure.

    There is no single "right way" to represent network data with graphs. There are a few basic rules, and we reviewed these in the previous chapter. Different ways of drawing pictures of network data can emphasize (or obscure) different features of the social structure. It's usually a good idea to play with visualizing a network, to experiment and be creative. There are a number of software tools that are available for drawing graphs, and each has certain strengths and limitations. In this chapter, we will look at some commonly used techniques for visualizing graphs using NetDraw (version 4.14, which is distributed along with UCINET). There are many other packages though, and you might want to explore some of the tools available in Pajek, and Mage (look for software at the web-site of the International Network of Social Network Analysts - INSNA).

    Of course, if there are a large number of actors or a large number of relations among them, pictures may not help the situation much; numerical indexes describing the graph may be the only choice. Numerical approaches and graphical approaches can be used in combination, though. For example, we might first calculate the "between-ness centrality" of the nodes in a large network, and then use graphs that include only those actors that have been identified as "important."


    This page titled 4.1: Introduction - A picture is worth... is shared under a not declared license and was authored, remixed, and/or curated by Robert Hanneman & Mark Riddle.

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