Statistical and Machine Learning Approaches for Network Analysis

Statistical and Machine Learning Approaches for Network Analysis (Wiley Series in Computational Statistics)

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

1 A Survey of Computational Approaches to Reconstruct and Partition Biological Networks 1
2 Introduction to Complex Networks: Measures, Statistical Properties, and Models 45
3 Modeling for Evolving Biological Networks 77
4 Modularity Configurations in Biological Networks with Embedded Dynamics 109
5 Influence of Statistical Estimators on the Large-Scale Causal Inference of Regulatory Networks 131
6 Weighted Spectral Distribution: A Metric for Structural Analysis of Networks 153
7 The Structure of an Evolving Random Bipartite Graph 191
8 Graph Kernels 217
9 Network-Based Information Synergy Analysis for Alzheimer Disease 245
10 Density-Based Set Enumeration in Structured Data 261
11 Hyponym Extraction Employing a Weighted Graph Kernel 303