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.
Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networks Offers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and opportunistic networks Demonstrates the practices for designing effective protocol/applications for next generation wireless networks Includes case studies showcasing the importance of properly understanding fundamental mobility model properties in wireless network performance evaluation
A unique, hands-on guide to interactive modeling and simulation of engineering systems This book describes advanced, cutting-edge techniques for dynamic system simulation using the DESIRE modeling/simulation software package. It offers detailed guidance on how to implement the software, providing scientists and engineers with powerful tools for creating simulation scenarios and experiments for such dynamic systems as aerospace vehicles, control systems, or biological systems. Along with two new chapters on neural networks, Advanced Dynamic-System Simulation, Second Edition revamps and updates all the material, clarifying explanations and adding many new examples. A bundled CD contains an industrial-strength version of OPEN DESIRE as well as hundreds of program examples that readers can use in their own experiments. The only book on the market to demonstrate model replication and Monte Carlo simulation of real-world engineering systems, this volume: Presents a newly revised systematic procedure for difference-equation modeling Covers runtime vector compilation for fast model replication on a personal computer Discusses parameter-influence studies, introducing very fast vectorized statistics computation Highlights Monte Carlo studies of the effects of noise and manufacturing tolerances for control-system modeling Demonstrates fast, compact vector models of neural networks for control engineering Features vectorized programs for fuzzy-set controllers, partial differential equations, and agro-ecological modeling Advanced Dynamic-System Simulation, Second Edition is a truly useful resource for researchers and design engineers in control and aerospace engineering, ecology, and agricultural planning. It is also an excellent guide for students using DESIRE.
A comprehensive, authoritative look at an emergent area in post-genomic science, Evolutionary genomics is an up-and-coming, complex field that attempts to explain the biocomplexity of the living world. Evolutionary Genomics and Systems Biology is the first full-length book to blend established and emerging concepts in bioinformatics, evolution, genomics, and structural biology, with the integrative views of network and systems biology. Three key aspects of evolutionary genomics and systems biology are covered in clear detail: the study of genomic history, i.e., understanding organismal evolution at the genomic level; the study of macromolecular complements, which encompasses the evolution of the protein and RNA machinery that propels life; and the evolutionary and dynamic study of wiring diagrams—macromolecular components in interaction—in the context of genomic complements. The book also features: A solid, comprehensive treatment of phylogenomics, the evolution of genomes, and the evolution of biological networks, within the framework of systems biology A special section on RNA biology—translation, evolution of structure, and micro RNA and regulation of gene expression Chapters on the mapping of genotypes to phenotypes, the role of information in biology, protein architecture and biological function, chromosomal rearrangements, and biological networks and disease Contributions by leading authorities on each topic Evolutionary Genomics and Systems Biology is an ideal book for students and professionals in genomics, bioinformatics, evolution, structural biology, complexity, origins of life, systematic biology, and organismal diversity, as well as those individuals interested in aspects of biological sciences as they interface with chemistry, physics, and computer science and engineering.