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Biological Network Analysis
Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes. The book's authors discuss various graph theoretic and data analytics approaches used to analyze these networks with respect to available tools, technologies, standards, algorithms and databases for generating, representing and analyzing graphical data. As a wide variety of algorithms have been developed to analyze and compare networks, this book is a timely resource. - Presents recent advances in biological network analysis, combining Graph Theory, Graph Analysis, and various network models - Discusses three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN) and Human Brain Connectomes - Includes a discussion of various graph theoretic and data analytics approaches
Models of Science Dynamics
Models of Science Dynamics aims to capture the structure and evolution of science, the emerging arena in which scholars, science and the communication of science become themselves the basic objects of research. In order to capture the essence of phenomena as diverse as the structure of co-authorship networks or the evolution of citation diffusion patterns, such models can be represented by conceptual models based on historical and ethnographic observations, mathematical descriptions of measurable phenomena, or computational algorithms. Despite its evident importance, the mathematical modeling of science still lacks a unifying framework and a comprehensive study of the topic. This volume fill...
Complex Networks
This book constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on Complex Networks, CompleNet 2010, which was held in Rio de Janeiro, Brazil, in October 2010. The 21 revised papers presented were carefully reviewed and selected from 48 submissions. The topics covered include: community structure, network metrics, network models, effect of topology to epidemics, algorithms to classify networks, self-organized algorithms applied to complex networks, as well as many applications of complex networks in biology, image analysis, software development, traffic congestion, language and speech, sensor networks, and synchronization.
Beyond Capital
The financial/social cataclysm beginning in 2007 ended notions of a “great moderation” and the view that capitalism had overcome its systemic tendencies to crisis. The subsequent failure of contemporary social formations to address the causes of the crisis gives renewed impetus to better analysis in aid of the search for a better future. This book contributes to this search by reviving a broad discussion of what we humans might want a post-capitalist future to be like. It argues for a comparative anthropological critique of capital notions of value, thereby initiating the search for a new set of values, as well as identifying a number of selected computing practices that might evoke new ...
Complex Networks
A comprehensive introduction to the theory and applications of complex network science, complete with real-world data sets and software tools.
Quantitative Analysis of Dependency Structures
Dependency analysis is increasingly used in computational linguistics and cognitive science. Surprisingly, compared with studies based on phrase structures, quantitative methods and dependency structure are rarely integrated in research.This is the first book that collects original contributions which quantitatively analyze dependency structures across different languages and text genres.
Artificial Neural Networks and Machine Learning - ICANN 2011
This two volume set (LNCS 6791 and LNCS 6792) constitutes the refereed proceedings of the 21th International Conference on Artificial Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011. The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and applications.
Multiplex and Multilevel Networks
The science of networks represented a substantial change in the way we see natural and technological phenomena. Now we have a better understanding that networks are, in most cases, networks of networks or multi-layered networks. This book provides a summary of the research done during one of the largest and most multidisciplinary projects in network science and complex systems (Multiplex). The science of complex networks originated from the empirical evidence that most of the structures of systems such as the internet, sets of protein interactions, and collaboration between people, share (at least qualitatively) common structural properties. This book examines how properties of networks that interact with other networks can change dramatically. The authors show that, dependent on the properties of links that interconnect two or more networks, we may derive different conclusions about the function and the possible vulnerabilities of the overall system of networks. This book presents a series of novel theoretical results together with their applications, providing a comprehensive overview of the field.
The State of Economics, the State of the World
Leading economists address the ongoing challenges to economics in theory and practice in a time of political and economic crises. More than a decade of financial crises, sovereign debt problems, political conflict, and rising xenophobia and protectionism has left the global economy unsettled and the ability of economics as a discipline to account for episodes of volatility uncertain. In this book, leading economists consider the state of their discipline in a world of ongoing economic and political crises. The book begins with three sweeping essays by Nobel laureates Kenneth Arrow (in one of his last published works), Amartya Sen, and Joseph Stiglitz that offer a summary of the theoretical f...
Data Science for Mathematicians
Mathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science. As it applies to the curriculum, research, and career opportunities, data science is a fast-growing field. Contributors from both academics and industry present their views on these opportunities and how to advantage them.
