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Exponential Random Graph Models for Social Networks

Exponential random graph models (ERGMs) are increasingly applied to observed network data and are central to understanding social structure;

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Exponential Random Graph Models for Social Networks

Exponential random graph models (ERGMs) are increasingly applied to observed network data and are central to understanding social structure;

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An Introduction to Exponential Random Graph Modeling

Explaining the techniques and applications of exponential random graph modeling (ERGM) for social scientists, this is a uniquely;

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Random Networks for Communication

fascinating synthesis of random graph theory, stochastic geometry and percolation theory to provide models for both structure and information flow;

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Structural Analysis in the Social Sciences

, exponential families of random graphs, the analysis of longitudinal network data, graphical techniques for exploring network data, and software for;

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Structural Analysis in the Social Sciences

, exponential families of random graphs, the analysis of longitudinal network data, graphical techniques for exploring network data, and software for;

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Algorithms and Models for the Web Graph

This book constitutes the refereed proceedings of the 9th International Workshop on Algorithms and Models for the Web-Graph, WAW 2012, held;

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Large Deviations for Random Graphs

This book addresses the emerging body of literature on the study of rare events in random graphs and networks. For example, what does a;

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Generating Random Networks and Graphs

theoretical study. This book presents and discusses common methods of generating random graphs. It begins with approaches such as Exponential Random;

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Networks

; fundamentals of graph theory; computer algorithms; mathematical models of networks, including random graph models and generative models; and theories;

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Algorithms and Models for the Web Graph

This book constitutes the refereed proceedings of the 10th International Workshop on Algorithms and Models for the Web Graph, WAW 2013;

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Networks

mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most;

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Algorithms and Models for the Web Graph

This book constitutes the proceedings of the 13th International Workshop on Algorithms and Models for the Web Graph, WAW 2016, held;

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Graph Representation Learning

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry;

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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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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

recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information;

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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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Intelligent Random Walk

social networks. The book opens new horizons for designing prediction models and problem-solving methods based on intelligent random walk;

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Graph Theoretic Methods in Multiagent Networks

swarming, quantum networks, networked economics, biological synchronization, and social networks. Focusing on graph theoretic methods for the;

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Statistical Analysis of Graph Structures in Random Variable Networks

of algorithms of network structure identification with applications to market network analysis. For this, a mathematical model of random variable;

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Graph Data Mining

applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used;

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Algebraic and Geometric Methods in Discrete Mathematics

; applications of representation theory to voting theory and game theory; a study of fixed points of tensors; and exponential random graph models from;

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Statistical and Machine Learning Approaches for Network Analysis

for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on;

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Performance Analysis of Queuing and Computer Networks

stochastic processes. After an introduction to queues in computer networks, this self-contained book covers important random variables, such as;

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Performance Analysis of Queuing and Computer Networks

stochastic processes. After an introduction to queues in computer networks, this self-contained book covers important random variables, such as;

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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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