Research and Markets: Statistical and Machine Learning Approaches for Network Analysis. Wiley Series in Computational Statistics

DUBLIN--(BUSINESS WIRE)-- Research and Markets ( 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


Matthias Dehmer.

Subhash C. Basak.

For more information visit

Source: John Wiley and Sons Ltd

Research and Markets
Laura Wood, Senior Manager
U.S. Fax: 646-607-1907
Fax (outside U.S.): +353-1-481-1716
Sector: Mathematics

Source: Research and Markets