| Author | : Steve Horvath |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 433 |
| Release | : 2011-04-30 |
| Genre | : Science |
| ISBN | : 144198819X |
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 | : Steve Horvath |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2014-10-01 |
| Genre | : Science |
| ISBN | : 9781493900220 |
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 | : Steve Horvath |
| Publisher | : Springer |
| Total Pages | : 421 |
| Release | : 2011-05-26 |
| Genre | : Science |
| ISBN | : 9781441988201 |
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 |
| Total Pages | : 496 |
| Release | : 2016-03-04 |
| Genre | : Medical |
| ISBN | : 0124081185 |
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. - Winner of the 2017 PROSE Award in Biomedicine & Neuroscience and the 2017 British Medical Association (BMA) Award in Neurology - 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 | : Dmitry Zinoviev |
| Publisher | : Pragmatic Bookshelf |
| Total Pages | : 330 |
| Release | : 2018-01-19 |
| Genre | : Computers |
| ISBN | : 1680505408 |
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 | : Ronaldo Menezes |
| Publisher | : Springer |
| Total Pages | : 265 |
| Release | : 2012-07-27 |
| Genre | : Technology & Engineering |
| ISBN | : 3642302874 |
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 | : Ireneusz Czarnowski |
| Publisher | : Springer Nature |
| Total Pages | : 525 |
| Release | : 2020-06-11 |
| Genre | : Technology & Engineering |
| ISBN | : 9811559252 |
This book gathers selected papers from the KES-IDT-2020 Conference, held as a Virtual Conference on June 17–19, 2020. The aim of the annual conference was to present and discuss the latest research results, and to generate new ideas in the field of intelligent decision-making. However, the range of topics discussed during the conference was definitely broader and covered methods in e.g. classification, prediction, data analysis, big data, data science, decision support, knowledge engineering, and modeling in such diverse areas as finance, cybersecurity, economics, health, management and transportation. The Problems in Industry 4.0 and IoT are also addressed. The book contains several sections devoted to specific topics, such as Intelligent Data Processing and its Applications High-Dimensional Data Analysis and its Applications Multi-Criteria Decision Analysis – Theory and Applications Large-Scale Systems for Intelligent Decision-Making and Knowledge Engineering Decision Technologies and Related Topics in Big Data Analysis of Social and Financial Issues Decision-Making Theory for Economics
| Author | : Mark R. T. Dale |
| Publisher | : Cambridge University Press |
| Total Pages | : 250 |
| Release | : 2021-04-15 |
| Genre | : Nature |
| ISBN | : 1108632971 |
Network thinking and network analysis are rapidly expanding features of ecological research. Network analysis of ecological systems include representations and modelling of the interactions in an ecosystem, in which species or factors are joined by pairwise connections. This book provides an overview of ecological network analysis including generating processes, the relationship between structure and dynamic function, and statistics and models for these networks. Starting with a general introduction to the composition of networks and their characteristics, it includes details on such topics as measures of network complexity, applications of spectral graph theory, how best to include indirect species interactions, and multilayer, multiplex and multilevel networks. Graduate students and researchers who want to develop and understand ecological networks in their research will find this volume inspiring and helpful. Detailed guidance to those already working in network ecology but looking for advice is also included.
| Author | : Jeroen Bruggeman |
| Publisher | : Routledge |
| Total Pages | : 249 |
| Release | : 2013-05-13 |
| Genre | : Social Science |
| ISBN | : 113404819X |
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.
