$$\newcommand{\id}{\mathrm{id}}$$ $$\newcommand{\Span}{\mathrm{span}}$$ $$\newcommand{\kernel}{\mathrm{null}\,}$$ $$\newcommand{\range}{\mathrm{range}\,}$$ $$\newcommand{\RealPart}{\mathrm{Re}}$$ $$\newcommand{\ImaginaryPart}{\mathrm{Im}}$$ $$\newcommand{\Argument}{\mathrm{Arg}}$$ $$\newcommand{\norm}[1]{\| #1 \|}$$ $$\newcommand{\inner}[2]{\langle #1, #2 \rangle}$$ $$\newcommand{\Span}{\mathrm{span}}$$

# 4: Working with Netdraw to Visualize Graphs

$$\newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} }$$ $$\newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}}$$$$\newcommand{\id}{\mathrm{id}}$$ $$\newcommand{\Span}{\mathrm{span}}$$ $$\newcommand{\kernel}{\mathrm{null}\,}$$ $$\newcommand{\range}{\mathrm{range}\,}$$ $$\newcommand{\RealPart}{\mathrm{Re}}$$ $$\newcommand{\ImaginaryPart}{\mathrm{Im}}$$ $$\newcommand{\Argument}{\mathrm{Arg}}$$ $$\newcommand{\norm}[1]{\| #1 \|}$$ $$\newcommand{\inner}[2]{\langle #1, #2 \rangle}$$ $$\newcommand{\Span}{\mathrm{span}}$$ $$\newcommand{\id}{\mathrm{id}}$$ $$\newcommand{\Span}{\mathrm{span}}$$ $$\newcommand{\kernel}{\mathrm{null}\,}$$ $$\newcommand{\range}{\mathrm{range}\,}$$ $$\newcommand{\RealPart}{\mathrm{Re}}$$ $$\newcommand{\ImaginaryPart}{\mathrm{Im}}$$ $$\newcommand{\Argument}{\mathrm{Arg}}$$ $$\newcommand{\norm}[1]{\| #1 \|}$$ $$\newcommand{\inner}[2]{\langle #1, #2 \rangle}$$ $$\newcommand{\Span}{\mathrm{span}}$$

A lot of the work that we do with social networks is primarily descriptive and/or exploratory, rather than confirmatory hypothesis testing. Using some of the tools described in this chapter can be particularly helpful because they may let you see patterns that you might not otherwise have seen. The tools can be used to explore tentative empirical generalizations and provide crude first examinations of hypotheses about patterns that may be present in the data. Some of the tools are also very helpful for dealing with the complexity of social network data, which may involve many actors, many ties, and several types of ties. Hiding, highlighting, and locating parts of the data can be a big help in making sense of the data. In some cases (like ego networks and the evolution of networks over time) hiding and revealing parts of the data are critical to understanding and describing the construction and evolution of the social structures.

• 4.1: Introduction - A picture is worth...
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?
• 4.2: Node Attributes
It is often the case that the structure of a network depends on the attributes of the actors embedded in it. If we are looking at the network of "spouse" ties among Bob, Carol, Ted, and Alice, one would note that ties exist for male-female pairs, but not (in our example) for female-female or male-male pairs. Being able to visualize the locations of different types of actors in a graph can help us see patterns, and to understand the nature of the social processes that generated the tie structure.
• 4.3: Relation Properties
A graph, as we discussed in the last chapter, is made up of both the actors and the relations among the actors. The relations among the actors (the line segments in a simple graph or the arrows in a directed graph) can also have "attributes." Sometimes it can be very helpful to use color and size to indicate difference of kind and amount among the relations. You can be creative with this idea to explore and display patterns in the connections among the actors in a network.
• 4.4: Location, location, location
Most graphs of networks are drawn in a two-dimensional "X-Y axis" space (Mage and some other packages allow 3-dimensional rendering and rotation). Where a node or a relation is drawn in the space is essentially arbitrary -- the full information about the network is contained in its list of nodes and relations. The figures below are exactly the same network (Knoke's money flow network) that has been rendered in several different ways.
• 4.5: Highlighting parts of the network
Large networks (those that contain many actors, many kinds of relations, and/or high densities of ties) can be very difficult to visualize in any useful way -- there is simply too much information. Often, we need to clear away some of the "clutter" to see main patterns more clearly. One of the most interesting features of social networks -- whether small or large -- is the extent to which we can locate "local sub-structures."
• 4.6: A few hints on data handling with NetDraw
There are several ways to get data into NetDraw.