A User's Guide to Network Analysis in R (Use R!) by Douglas Luke

By Douglas Luke

Providing a finished source for the mastery of community research in R, the target of community research with R is to introduce smooth community research innovations in R to social, actual, and well-being scientists. The mathematical foundations of community research are emphasised in an available means and readers are guided throughout the uncomplicated steps of community reviews: community conceptualization, facts assortment and administration, community description, visualization, and construction and checking out statistical types of networks. as with any of the books within the Use R! sequence, every one bankruptcy comprises wide R code and special visualizations of datasets. Appendices will describe the R community applications and the datasets utilized in the e-book. An R package deal constructed particularly for the booklet, to be had to readers on GitHub, includes appropriate code and real-world community datasets in addition.

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Maximize the symmetry of the layout of nodes. Minimize the variability of the edge lengths. Maximize the angle between edges when they cross or join nodes. Minimize the total space used for the network display. A large number of approaches have been developed for automatic layout of network graphics. One general class of algorithms, called force-directed, has proven to be a flexible and powerful approach to automatic network layouts. These algorithms work iteratively to minimize the total energy in a network, where the energy can be defined in a number of ways.

For more detailed information about network objects in statnet, see Butts (2008). 1 Creating a Network Object in statnet To create a network object, the identically-named network() function is called. This function has a number of options, but the most common way to use it is to feed relational data to it–typically an adjacency matrix or edge list. To see how this works we will continue with the example directed network from Fig. 1. First, we will create a network using an adjacency matrix. names: character valued attribute 5 valid vertex names No edge attributes Network A B C A 0 1 1 B 0 0 1 C 0 1 0 D 0 0 0 E 0 0 1 adjacency matrix: D E 0 0 1 0 0 0 0 0 0 0 The results of the class() and summary() calls show that we have successfully created a new network object.

4 Common Network Data Tasks The preceding sections covered the basic information needed to create and manage network data objects in R. However, the data managements tasks for network analysis do not end there. There are any number of network analytic challenges that will require more sophisticated data management and transformation techniques. In the rest of this chapter, two such examples are covered: preparing subsets of network data for analysis by filtering on node and edge characteristics, and turning directed networks into non-directed networks.

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