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Complex Graphs and Networks

first principles the universal and ubiquitous coherence in the structure of these realistic but complex networks? In order to analyze these;

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Graphs and Networks

Graphs and Networks A unique blend of graph theory and network science for mathematicians and data science professionals alike;

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Graph Theory and Interconnection Networks

provides a fundamental tool for designing and analyzing such networks. Graph Theory and Interconnection Networks provides a thorough understanding;

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Graph Theory and Interconnection Networks

provides a fundamental tool for designing and analyzing such networks. Graph Theory and Interconnection Networks provides a thorough understanding;

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Optimization Algorithms for Networks and Graphs

A revised and expanded advanced-undergraduate/graduate text (first ed., 1978) about optimization algorithms for problems that can be;

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Optimization Algorithms for Networks and Graphs

A revised and expanded advanced-undergraduate/graduate text (first ed., 1978) about optimization algorithms for problems that can be;

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Graph Mining

network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks;

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Computational Ecology: Graphs, Networks And Agent-based Modeling

Graphs, networks and agent-based modeling are the most thriving and attracting sciences used in ecology and environmental sciences. As such;

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Algorithms and Models for the Web Graph

This book constitutes the proceedings of the 14th International Workshop Algorithms and Models for the Web Graph, WAW 2017, held in Toronto;

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Bayesian Networks and Decision Graphs

This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical;

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Graphs and Networks

This book concerns the use of graphs for the simulation and representation of transport networks. Its aim is to cover networks in both;

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Beyond Planar Graphs

This book is the first general and extensive review on the algorithmics and mathematical results of beyond planar graphs. Most real-world;

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Symmetries of Nonlinear PDEs on Metric Graphs and Branched Networks

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Community Search over Big Graphs

, biological, collaboration, and communication networks. Recently, community search over graphs has attracted significantly increasing attention;

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Machine Learning in Social Networks

This book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing;

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Algorithms and Models for the Web Graph

This book constitutes the refereed proceedings of the 11th International Workshop on Algorithms and Models for the Web Graph, WAW 2014;

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Probability on Trees and Networks

, eigenvalues, transition probabilities, and random walks. Written by two leading researchers, the text emphasizes intuition, while giving complete;

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Probability on Trees and Networks

, eigenvalues, transition probabilities, and random walks. Written by two leading researchers, the text emphasizes intuition, while giving complete;

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Generating Random Networks and Graphs

Generating random networks efficiently and accurately is an important challenge for practical applications, and an interesting question for;

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Spectral Clustering And Biclustering

Explores regular structures in graphs and contingency tables by spectral theory and statistical methods This book bridges the gap between;

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Graphs

This adaptation of an earlier work by the authors is a graduate text and professional reference on the fundamentals of graph theory. It;

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Structural Analysis in the Social Sciences

Models and Methods in Social Network Analysis, first published in 2005, presents the most important developments in quantitative models and;

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Structural Analysis in the Social Sciences

Models and Methods in Social Network Analysis, first published in 2005, presents the most important developments in quantitative models and;

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Introduction to Graph Neural Networks

, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that;

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Introduction to Graph Neural Networks

This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with;

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Random Graphs and Complex Networks

empirical properties and a wealth of new models have emerged to capture them. Classroom tested for over ten years, this text places recent advances;

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On Uncertain Graphs

Large-scale, highly interconnected networks, which are often modeled as graphs, pervade both our society and the natural world around us;

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