In the modern world of gigantic datasets, which scientists and practioners ofall fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way. This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. INDICE: Introduction to Complex Networks.- Standard Approaches to Network Structure: Block Modeling.- A First Principles Approach to Block Structure Detection.- Diagonal Block Models as Cohesive Groups.- Modularity of Dense RandomGraphs.- Modularity of Sparse Random Graphs.- Applications.- Conclusion and Outlook.- References.
- ISBN: 978-3-540-87832-2
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 180
- Fecha Publicación: 01/11/2008
- Nº Volúmenes: 1
- Idioma: Inglés