DUBLIN--(BUSINESS WIRE)-- Research and Markets (http://www.researchandmarkets.com/research/tb5cnk/statistical_and) has announced the addition of John Wiley and Sons Ltd's new book "Statistical and Machine Learning Approaches for Network Analysis. Wiley Series in Computational Statistics" to their offering.
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.
Key Topics Covered:
Chapter 1. A Survey of Computational Approaches to Reconstruct and Partition Biological Networks
Chapter 2. Introduction to Complex Networks: Measures, Statistical Properties, and Models
Chapter 3. Modeling for Evolving Biological Networks
Chapter 4. Modularity Configurations in Biological Networks with Embedded Dynamics
Chapter 5. Influence of Statistical Estimators on the Large Scale Causal Inference of Regulatory Networks
Chapter 6. Weighted Spectral Distribution: A Metric for Structural Analysis of Networks
Chapter 7. The Structure of an Evolving Random Bipartite Graph
Chapter 8. Graph Kernels
Chapter 9. Network-based information synergy analysis for Alzheimer disease
Chapter 10. Density-Based Set Enumeration in Structured Data
Chapter 11. Hyponym Extraction Employing a Weighted Graph Kernel
Subhash C. Basak.
For more information visit http://www.researchandmarkets.com/research/tb5cnk/statistical_and
Source: John Wiley and Sons Ltd
Source: Research and Markets