Statistical and Machine Learning Approaches for Network Analysis

ISBN-13: 9780470195154 / Angielski / Twarda / 2012 / 344 str.

Matthias Dehmer; Subhash C. Basak

Opis

This text 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.

Spis treści

Preface ix

Contributors xi

1 A Survey of Computational Approaches to Reconstruct and Partition Biological Networks 1
Lipi Acharya, Thair Judeh, and Dongxiao Zhu

2 Introduction to Complex Networks: Measures, Statistical Properties, and Models 45
Kazuhiro Takemoto and Chikoo Oosawa

3 Modeling for Evolving Biological Networks 77
Kazuhiro Takemoto and Chikoo Oosawa

4 Modularity Configurations in Biological Networks with Embedded Dynamics 109
Enrico Capobianco, Antonella Travaglione, and Elisabetta Marras

5 Influence of Statistical Estimators on the Large–Scale Causal Inference of Regulatory Networks 131
Ricardo de Matos Simoes and Frank Emmert–Streib

6 Weighted Spectral Distribution: A Metric for Structural Analysis of Networks 153
Damien Fay, Hamed Haddadi, Andrew W. Moore, Richard Mortier, Andrew G. Thomason, and Steve Uhlig

7 The Structure of an Evolving Random Bipartite Graph 191
Reinhard Kutzelnigg

8 Graph Kernels 217
Matthias Rupp

9 Network–Based Information Synergy Analysis for Alzheimer Disease 245
Xuewei Wang, Hirosha Geekiyanage, and Christina Chan

10 Density–Based Set Enumeration in Structured Data 261
Elisabeth Georgii and Koji Tsuda

11 Hyponym Extraction Employing a Weighted Graph Kernel 303
Tim vor der Br¨uck

Index 327

O autorze

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.

Blurb

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.