Dehmer / Basak Statistical and Machine Learning Approaches for Network Analysis

1. Auflage 2012
ISBN: 978-1-118-34698-3
Verlag: John Wiley & Sons
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 344 Seiten, E-Book

Reihe: Wiley Series in Computational Statistics

Explore the multidisciplinary nature of complex networksthrough machine learning techniques
Statistical and Machine Learning Approaches for NetworkAnalysis provides an accessible framework for structurallyanalyzing graphs by bringing together known and novel approaches ongraph classes and graph measures for classification. By providingdifferent approaches based on experimental data, the book uniquelysets itself apart from the current literature by exploring theapplication of machine learning techniques to various types ofcomplex networks.
Comprised of chapters written by internationally renownedresearchers in the field of interdisciplinary network theory, thebook presents current and classical methods to analyze networksstatistically. Methods from machine learning, data mining, andinformation theory are strongly emphasized throughout. Real datasets are used to showcase the discussed methods and topics, whichinclude:
* A survey of computational approaches to reconstruct andpartition biological networks
* An introduction to complex networks--measures, statisticalproperties, 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 NetworkAnalysis is an excellent supplemental text for graduate-level,cross-disciplinary courses in applied discrete mathematics,bioinformatics, pattern recognition, and computer science. The bookis also a valuable reference for researchers and practitioners inthe fields of applied discrete mathematics, machine learning, datamining, and biostatistics.

Autoren/Hrsg.

Dehmer, Matthias

Basak, Subhash C.

Inhaltsverzeichnis

Preface ix
Contributors xi
1 A Survey of Computational Approaches to Reconstruct andPartition Biological Networks 1
Lipi Acharya, Thair Judeh, and Dongxiao Zhu
2 Introduction to Complex Networks: Measures, StatisticalProperties, 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 EmbeddedDynamics 109
Enrico Capobianco, Antonella Travaglione, and ElisabettaMarras
5 Influence of Statistical Estimators on the Large-Scale CausalInference of Regulatory Networks 131
Ricardo de Matos Simoes and Frank Emmert-Streib
6 Weighted Spectral Distribution: A Metric for StructuralAnalysis 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 AlzheimerDisease 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 Kernel303
Tim vor der Br¨uck
Index 327

Über Autor(innen)

MATTHIAS DEHMER, PhD, is Head of the Institute forBioinformatics and Trans- lational Research at the University forHealth Sciences, Medical Informatics and Technology (Austria). Hehas written over 130 publications in his research areas, whichinclude bioinformatics, systems biology, and applied discretemathematics. Dr. Dehmer is also the coeditor of AppliedStatistics for Network Biology, Statistical Modelling of MolecularDescriptors in QSAR/QSPR, Medical Biostatistics for ComplexDiseases, Analysis of Complex Networks, and Analysis ofMicroarray Data, all published by Wiley.
SUBHASH C. BASAK, PhD, is Senior Research Associate atthe Natural Resources Research Institute. He has publishedextensively in the areas of biochemical pharmacology, toxicology,mathematical chemistry, and computational chemistry.