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.
Table of Contents:
Chapter 1. A Survey of Computational Approaches to Reconstruct and Partition Biological Networks Acharya et al. Chapter 2. Introduction to Complex Networks: Measures, Statistical Properties, and Models Takemoto et al. Chapter 3. Modeling for Evolving Biological Networks Takemoto et al. Chapter 4. Modularity Configurations in Biological Networks with Embedded Dynamics Capobianco et al. Chapter 5. Influence of Statistical Estimators on the Large Scale Causal Inference of Regulatory Networks Matos de Simoes and Emmert-Streib Chapter 6. Weighted Spectral Distribution: A Metric for Structural Analysis of Networks Fay, Haddadi et al. Chapter 7. The Structure of an Evolving Random Bipartite Graph Kutzelnigg Chapter 8. Graph Kernels Rupp Chapter 9. Network-based information synergy analysis for Alzheimer disease Wang, Geekiyanage and Chan Chapter 10. Density-Based Set Enumeration in Structured Data Georgii and Tsuda Chapter 11. Hyponym Extraction Employing a Weighted Graph Kernel Vor der Bruck
About the Author :
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.
