Statistical and Machine Learning Approaches for Network Analysis

This book uniquely focuses on graph mining and classification techniques and introduces novel graph classes appropriate for countless applications across many disciplines. It explores the relationship of novel graph classes among each other; existing and classical methods to analyze networks; similarity and classification techniques based on machine learning methods; and applications of classification and mining. With emphasis on computational aspects such as machining learning, data mining, and information theory techniques, this book will benefit both professionals and graduate-level students in the field.

Matthias Dehmer, PhD, is Head of the Institute for Bioinformatics and Transnational 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.