6: Working with Network Data
- Page ID
- 7678
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This chapter is about the kinds of "data structures" that network analysts work with most frequently, and some of the most common kinds of transformations and manipulations of these structures.
- 6.1: Manipulating Network Data Structures
- Earlier, we emphasized that the social network perspective leads us to focus our attention on the relations between actors, more than on the attributes of actors. This approach often results in data that have a different "structure" in which both rows and columns refer to the same actors, and the cells report information on one variable that describes variation in the relations between each pair of actors
- 6.2: Making UCINET Datasets
- UCINET datasets are stored in a special (Pascal) format, but can be created and manipulated using both UCINET's and other software tools (text editors and spreadsheets). Each UCINET dataset consists of two separate files that contain header information (e.g. myfile.##h) and the data lines (e.g. myfile.##d). Because of this somewhat unusual way of storing data, it is best to create data sets with the internal spreadsheet editor or DL language tools, or to import text or spreadsheet files.
- 6.3: Transforming Data Values
- It is not at all unusual for the analyst to want to change the values that describe the relations between actors, or the values that describe the attributes of actors.
- 6.4: File Handling Tools
- Because UCINET data files are stored in a somewhat unusual dual-file format, it is usually most convenient to do basic file-handling tasks within UCINET. The program has basic file handling tools within it. If you use file handling commands outside UCINET (e.g. using Windows), you need to remember to deal with both files for each data set.
- 6.5: Selecting Subsets of the Data
- As we work on understanding the structure of a social network, there are occasions when we may wish to focus our attention on only a portion of the actors. Sometimes it's just a matter of clearing away "underbrush" of nodes that aren't "important". Sometimes it's a matter of selecting sets of actors for separate consideration.
- 6.6: Making New Kinds of Graphs from Existing Graphs
- Network analysis often finds it useful to see actor attributes as actually indicating the presence, absence, or strength of "relations" among actors. Suppose two persons have the same gender. To the non-network analyst, this may represent a statistical regularity that describes the frequencies of scores on a variable. A network analyst, though, might interpret the same data a bit differently. A network analyst might, instead, say "these two persons share the relation of having the same gender".