Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
415558 | Computational Statistics & Data Analysis | 2007 | 10 Pages |
Abstract
In the era of globalization, traditional theories and models of social systems are shifting their focus from isolation and independence to networks and connectedness. Analyzing these new complex social models is a growing, and computationally demanding area of research. In this study, we investigate the integration of genetic algorithms (GAs) with a random-walk-based distance measure to find subgroups in social networks. We test our approach by synthetically generating realistic social network data sets. Our clustering experiments using random-walk-based distances reveal exceptionally accurate results compared with the experiments using Euclidean distances.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Aykut Firat, Sangit Chatterjee, Mustafa Yilmaz,