Skip to main content Accessibility help
×
Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-19T01:30:05.325Z Has data issue: false hasContentIssue false

6 - Optimizational Approach to Conventional Blockmodeling

Published online by Cambridge University Press:  13 January 2010

Patrick Doreian
Affiliation:
University of Pittsburgh
Vladimir Batagelj
Affiliation:
University of Ljubljana
Anuska Ferligoj
Affiliation:
University of Ljubljana
Get access

Summary

Our explicit treatment of blockmodeling starts here and continues to the end of the book. We remind readers that blockmodeling provides a way of discerning fundamental structures in networks and permits the delineation of role systems. As a practical matter, most of the prior empirical work on blockmodeling was done within an approach that we have characterized as conventional blockmodeling (Section 1.5), and it is our point of departure here. Given our broader objective of replacing all of the features of conventional blockmodeling with the features of what we have called generalized blockmodeling, this chapter breaks naturally into two parts. One contains the foundational ideas of blockmodeling and the other develops the essential foundations of generalized blockmodeling. An indirect method proceeds by analyzing some transformation of the network data, whereas a direct method works with the network data themselves. By the end of this chapter, we develop a direct method to replace the indirect method and an optimization approach for delineating and fitting blockmodels. In this chapter, we stay within the framework of using structural and regular equivalence. The development of new block types, together with new types of blockmodels, starts in Chapter 7.

CONVENTIONAL BLOCKMODELING

The procedural goal of blockmodeling is to identify, in a given network, clusters (classes) of units (actors) that share structural characteristics defined in terms of some relation R. Each cluster forms a position. The units within a cluster have the same or similar connection patterns. Clusters form a partition C = {C1,C2, …, Ck}, which is a special type of clustering of the set of units U (see Section 5.2).

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×