Book description
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.
MATTHIAS DEHMER, PhD, is Head of the Institute for
Bioinformatics and Trans- lational Research at the University for
Health Sciences, Medical Informatics and Technology (Austria). He has
written over 130 publications in his research areas, which include
bioinformatics, systems biology, and applied discrete mathematics. Dr.
Dehmer is also the coeditor of Applied Statistics for Network
Biology, Statistical Modelling of Molecular Descriptors in
QSAR/QSPR, Medical Biostatistics for Complex Diseases, Analysis of
Complex Networks, and Analysis of Microarray Data, all
published by Wiley.
SUBHASH C. BASAK, PhD, is Senior Research Associate at the
Natural Resources Research Institute. He has published extensively in
the areas of biochemical pharmacology, toxicology, mathematical
chemistry, and computational chemistry.
