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Complex Networks
This volume is devoted to the applications of techniques from statistical physics to the characterization and modeling of complex networks. The first two parts of the book concern theory and modeling of networks, the last two parts survey applications to a wide variety of natural and artificial networks. The tutorial reviews that form this book are aimed at students and newcomers to the field, and will also constitute a modern and comprehensive reference for experts. To this aim, all contributions have been carefully peer-reviewed not only for scientific content but also for self-consistency and readability.
Complex Networks VIII
This book collects the works presented at the 8th International Conference on Complex Networks (CompleNet) 2017 in Dubrovnik, Croatia, on March 21-24, 2017. CompleNet aims at bringing together researchers and practitioners working in areas related to complex networks. The past two decades has witnessed an exponential increase in the number of publications within this field. From biological systems to computer science, from economic to social systems, complex networks are becoming pervasive in many fields of science. It is this interdisciplinary nature of complex networks that CompleNet aims at addressing. 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 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 such as network controllability, social structure, online behavior, recommendation systems, and network structure.
Los Alamos Science
None
Computational Complexity and Statistical Physics
Computer science and physics have been closely linked since the birth of modern computing. In recent years, an interdisciplinary area has blossomed at the junction of these fields, connecting insights from statistical physics with basic computational challenges. Researchers have successfully applied techniques from the study of phase transitions to analyze NP-complete problems such as satisfiability and graph coloring. This is leading to a new understanding of the structure of these problems, and of how algorithms perform on them. Computational Complexity and Statistical Physics will serve as a standard reference and pedagogical aid to statistical physics methods in computer science, with a particular focus on phase transitions in combinatorial problems. Addressed to a broad range of readers, the book includes substantial background material along with current research by leading computer scientists, mathematicians, and physicists. It will prepare students and researchers from all of these fields to contribute to this exciting area.
Knowledge Guided Machine Learning
Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and d...
The Nature of Complex Networks
The Nature of Complex Networks provides a systematic introduction to the statistical mechanics of complex networks and the different theoretical achievements in the field that are now finding strands in common. The book presents a wide range of networks and the processes taking place on them, including recently developed directions, methods, and techniques. It assumes a statistical mechanics view of random networks based on the concept of statistical ensembles but also features the approaches and methods of modern random graph theory and their overlaps with statistical physics. This book will appeal to graduate students and researchers in the fields of statistical physics, complex systems, graph theory, applied mathematics, and theoretical epidemiology.
Algorithms in Bioinformatics
This book constitutes the refereed proceedings of the First International Workshop on Algorithms in Bioinformatics, WABI 2001, held in Aarhus, Denmark, in August 2001. The 23 revised full papers presented were carefully reviewed and selected from more than 50 submissions. Among the issues addressed are exact and approximate algorithms for genomics, sequence analysis, gene and signal recognition, alignment, molecular evolution, structure determination or prediction, gene expression and gene networks, proteomics, functional genomics, and drug design; methodological topics from algorithmics; high-performance approaches to hard computational problems in bioinformatics.
Graphs, Morphisms and Statistical Physics
Based on a March 2001 workshop, this collection explores connections between random graphs and percolation, between slow mixing and phase transition, and between graph morphisms and hard-constraint models. Topics of the 14 papers include efficient local search near phase transitions in combinatorial optimization, graph homomorphisms and long range action, recent results on parameterized H-colorings, the satisfiability of random k-Horn formulae, a discrete non-Pfaffian approach to the Ising problem, and chromatic numbers of products of tournaments. No indexes are provided. Annotation : 2004 Book News, Inc., Portland, OR (booknews.com).
Charting the Next Pandemic
This book provides an introduction to the computational and complex systems modeling of the global spreading of infectious diseases. The latest developments in the area of contagion processes modeling are discussed, and readers are exposed to real world examples of data-model integration impacting the decision-making process. Recent advances in computational science and the increasing availability of real-world data are making it possible to develop realistic scenarios and real-time forecasts of the global spreading of emerging health threats. The first part of the book guides the reader through sophisticated complex systems modeling techniques with a non-technical and visual approach, expla...
Networks, Crowds, and Markets
Are all film stars linked to Kevin Bacon? Why do the stock markets rise and fall sharply on the strength of a vague rumour? How does gossip spread so quickly? Are we all related through six degrees of separation? There is a growing awareness of the complex networks that pervade modern society. We see them in the rapid growth of the internet, the ease of global communication, the swift spread of news and information, and in the way epidemics and financial crises develop with startling speed and intensity. This introductory book on the new science of networks takes an interdisciplinary approach, using economics, sociology, computing, information science and applied mathematics to address fundamental questions about the links that connect us, and the ways that our decisions can have consequences for others.
