Weighted Network Analysis

Author : Steve Horvath
Publisher : Springer Science & Business Media
ISBN 13 : 144198819X
Total Pages : 433 pages
Book Rating : 4.95/5 ( download)

DOWNLOAD NOW!

Download or read book Weighted Network Analysis written by Steve Horvath and published by Springer Science & Business Media. This book was released on 2011-04-30 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput measurements of gene expression and genetic marker data facilitate systems biologic and systems genetic data analysis strategies. Gene co-expression networks have been used to study a variety of biological systems, bridging the gap from individual genes to biologically or clinically important emergent phenotypes.

Author : Alex Fornito
Publisher : Academic Press
ISBN 13 : 0124081185
Total Pages : 494 pages
Book Rating : 4.85/5 ( download)

DOWNLOAD NOW!

Download or read book Fundamentals of Brain Network Analysis written by Alex Fornito and published by Academic Press. This book was released on 2016-03-04 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain

Author : Ronaldo Menezes
Publisher : Springer
ISBN 13 : 3642302874
Total Pages : 265 pages
Book Rating : 4.79/5 ( download)

DOWNLOAD NOW!

Download or read book Complex Networks written by Ronaldo Menezes and published by Springer. This book was released on 2012-07-27 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decade we have seen the emergence of a new inter-disciplinary field concentrating on the understanding large networks which are dynamic, large, open, and have a structure that borders order and randomness. The field of Complex Networks has helped us better understand many complex phenomena such as spread of decease, protein interaction, social relationships, to name but a few. The field of Complex Networks has received a major boost caused by the widespread availability of huge network data resources in the last years. One of the most surprising findings is that real networks behave very distinct from traditional assumptions of network theory. Traditionally, real networks were supposed to have a majority of nodes of about the same number of connections around an average. This is typically modeled by random graphs. But modern network research could show that the majority of nodes of real networks is very low connected, and, by contrast, there exists some nodes of very extreme connectivity (hubs). The current theories coupled with the availability of data makes the field of Complex Networks (sometimes called Network Sciences) one of the most promising interdisciplinary disciplines of today. This sample of works in this book gives as a taste of what is in the horizon such controlling the dynamics of a network and in the network, using social interactions to improve urban planning, ranking in music, and the understanding knowledge transfer in influence networks.

Author : Jeroen Bruggeman
Publisher : Routledge
ISBN 13 : 1134048203
Total Pages : 205 pages
Book Rating : 4.05/5 ( download)

DOWNLOAD NOW!

Download or read book Social Networks written by Jeroen Bruggeman and published by Routledge. This book was released on 2013-05-13 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social Networks: An Introduction is the first textbook that combines new with still-valuable older methods and theories. Designed to be a core text for graduate (and some undergraduate) courses in a variety of disciplines it is well-suited for everybody who makes a first encounter with the field of social networks, both academics and practitioners. This book includes reviews, study questions and text boxes as well as using innovative pedagogy to explain mathematical models and concepts. Examples ranging from anthropology to organizational sociology and business studies ensure wide applicability. An easy to use software tool, free of charge and open source, is appended on the supporting website that enables readers to depict and analyze networks of their interest. It is essential reading for students in sociology, anthropology, and business studies and can be used as secondary material for courses in economics and political science.

Author : Dmitry Zinoviev
Publisher : Pragmatic Bookshelf
ISBN 13 : 1680505408
Total Pages : 343 pages
Book Rating : 4.05/5 ( download)

DOWNLOAD NOW!

Download or read book Complex Network Analysis in Python written by Dmitry Zinoviev and published by Pragmatic Bookshelf. This book was released on 2018-01-19 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.

Author : Matthias Dehmer
Publisher : John Wiley & Sons
ISBN 13 : 111834698X
Total Pages : 269 pages
Book Rating : 4.83/5 ( download)

DOWNLOAD NOW!

Download or read book Statistical and Machine Learning Approaches for Network Analysis written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2012-06-26 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Sergio Consoli
Publisher : Springer Nature
ISBN 13 : 3030668916
Total Pages : 357 pages
Book Rating : 4.14/5 ( download)

DOWNLOAD NOW!

Download or read book Data Science for Economics and Finance written by Sergio Consoli and published by Springer Nature. This book was released on 2021 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.

Author : Hocine Cherifi
Publisher : Springer
ISBN 13 : 3319305697
Total Pages : 378 pages
Book Rating : 4.91/5 ( download)

DOWNLOAD NOW!

Download or read book Complex Networks VII written by Hocine Cherifi and published by Springer. This book was released on 2016-03-10 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last decades have seen the emergence of Complex Networks as the language with which a wide range of complex phenomena in fields as diverse as Physics, Computer Science, and Medicine (to name just a few) can be properly described and understood. This book provides a view of the state of the art in this dynamic field and covers topics ranging from network controllability, social structure, online behavior, recommendation systems, and network structure. This book includes the peer-reviewed list of works presented at the 7th Workshop on Complex Networks CompleNet 2016 which was hosted by the Université de Bourgogne, France, from March 23-25, 2016. The 28 carefully reviewed and selected contributions in this book address many topics related to complex networks and have been organized in seven major groups: (1) Theory of Complex Networks, (2) Multilayer networks, (3) Controllability of networks, (4) Algorithms for networks, (5) Community detection, (6) Dynamics and spreading phenomena on networks, (7) Applications of Networks.

Author : Artem Antonyuk
Publisher : Springer Nature
ISBN 13 : 303064877X
Total Pages : 366 pages
Book Rating : 4.70/5 ( download)

DOWNLOAD NOW!

Download or read book Networks in the Global World V written by Artem Antonyuk and published by Springer Nature. This book was released on 2021-02-19 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings book presents state-of-the-art developments in theory, methodology, and applications of network analysis across sociology, computational science, education research, literature studies, political science, international relations, social media research, and urban studies. The papers comprising this collection were presented at the Fifth ‘Networks in the Global World’ conference organized by the Centre for German and European Studies of St. Petersburg University and Bielefeld University and held on July 7–9, 2020. This biannual conference series revolves around key interdisciplinary issues in the focus of network analysts, such as the multidimensional approach to social reality, translation of theories and methods across disciplines, and mixing of data and methods. The distinctive features of this book are the emphasis on in-depth linkages between theory, method, and applications, the blend of qualitative and quantitative methods, and the joint consideration of different network levels, types, and contexts. The topics covered by the papers include interrelation of social and cultural structures, constellations of power, and patterns of interaction in areas ranging from various types of communities (local, international, educational, political, and so on) to social media and literature. The book is useful for practicing researchers, graduate and postgraduate students, and educators interested in network analysis of social relations, politics, economy, and culture. Features that set the book apart from others in the field: · The book offers a unique cross-disciplinary blend of computational and ethnographic network analyses applied to a diverse spectrum of spheres, from literature and education to urban planning and policymaking. · Embracing conceptual, methodological, and empirical works, the book is among the few in network analysis to emphasize connections between theory, method, and applications. · The book brings together authors and empirical contexts from all over the globe, with a particular emphasis on European societies.

Author : Applied Research Applied Research Press
Publisher :
ISBN 13 : 9781517392307
Total Pages : 42 pages
Book Rating : 4.06/5 ( download)

DOWNLOAD NOW!

Download or read book WGCNA: an R Package for Weighted Correlation Network Analysis written by Applied Research Applied Research Press and published by . This book was released on 2015-09-16 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.