Statistical and Machine Learning Approaches for Network Analysis

Explore the multidisciplinary nature of complex networks through machine learning techniquesStatistical 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 networksAn introduction to complex networks?measures, statistical properties, and modelsModeling for evolving biological networksThe structure of an evolving random bipartite graphDensity-based enumeration in structured dataHyponym extraction employing a weighted graph kernelStatistical 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.Content: Chapter 1 A Survey of Computational Approaches to Reconstruct and Partition Biological Networks (pages 1–43): Lipi Acharya, Thair Judeh and Dongxiao ZhuChapter 2 Introduction to Complex Networks: Measures, Statistical Properties, and Models (pages 45–75): Kazuhiro Takemoto and Chikoo OosawaChapter 3 Modeling for Evolving Biological Networks (pages 77–108): Kazuhiro Takemoto and Chikoo OosawaChapter 4 Modularity Configurations in Biological Networks with Embedded Dynamics (pages 109–129): Enrico Capobianco, Antonella Travaglione and Elisabetta MarrasChapter 5 Influence of Statistical Estimators on the Large?Scale Causal Inference of Regulatory Networks (pages 131–152): Ricardo de Matos Simoes and Frank Emmert?StreibChapter 6 Weighted Spectral Distribution: A Metric for Structural Analysis of Networks (pages 153–189): Damien Fay, Hamed Haddadi, Andrew W. Moore, Richard Mortier, Andrew G. Thomason and Steve UhligChapter 7 The Structure of an Evolving Random Bipartite Graph (pages 191–215): Reinhard KutzelniggChapter 8 Graph Kernels (pages 217–243): Matthias RuppChapter 9 Network?Based Information Synergy Analysis for Alzheimer Disease (pages 245–259): Xuewei Wang, Hirosha Geekiyanage and Christina ChanChapter 10 Density?Based Set Enumeration in Structured Data (pages 261–301): Elisabeth Georgii and Koji TsudaChapter 11 Hyponym Extraction Employing a Weighted Graph Kernel (pages 303–325): Tim vor der Bruck