E-raamat: Statistical and Machine Learning Approaches for Network Analysis

As the editors note in their preface, network analysis has become an emerging trend in several scientific disciplines. This work brings together a number of theoretical disciplines like graph theory, machine learning, and statistical data analysis to examine complex networks with an interdisciplinary approach using machine learning tools. Topics include a survey of computational approaches to reconstruct and partition biological networks, modeling for evolving biological networks, the structure of an evolving random bipartite graph, and network-based information synergy analysis for Alzheimer disease. The well-illustrated book includes extensive references, and while technical, the writing is direct. Editors are Dehmer (Institute for Bioinformatics and Transformational Research, U. for Health Sciences, Austria), and Basak (Natural Resources Research Institute). Annotation ©2012 Book News, Inc., Portland, OR (booknews.com)

Explore the multidisciplinary nature of complex networks through machine learning techniques

Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks.

Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include:

• A survey of computational approaches to reconstruct and partition biological networks
• An introduction to complex networks measures, statistical properties, and models
• Modeling for evolving biological networks
• The structure of an evolving random bipartite graph
• Density-based enumeration in structured data
• Hyponym extraction employing a weighted graph kernel

Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.