Network science : analysis and optimization algorithms for real-world applications

Network science analysis and optimization algorithms for real-world applications

Network Science Network Science offers comprehensive insight on network analysis and network optimization algorithms, with simple step-by-step guides and examples throughout, and a thorough introduction and history of network science, explaining the key concepts and the type of data needed for netwo...

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Detalles Bibliográficos
Otros Autores: Reis Pinheiro, Carlos Andre, 1940- author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, New Jersey : Wiley [2023]
Materias:
Computer networks.
Information networks.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009703307706719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Acknowledgments
  • About the Author
  • Chapter 1 Concepts in Network Science
  • 1.1 Introduction
  • 1.2 The Connector
  • 1.3 History
  • 1.3.1 A History in Social Studies
  • 1.4 Concepts
  • 1.4.1 Characteristics of Networks
  • 1.4.2 Properties of Networks
  • 1.4.3 Small World
  • 1.4.4 Random Graphs
  • 1.5 Network Analytics
  • 1.5.1 Data Structure for Network Analysis and Network Optimization
  • 1.5.1.1 Multilink and Self-Link
  • 1.5.1.2 Loading and Unloading the Graph
  • 1.5.2 Options for Network Analysis and Network Optimization Procedures
  • 1.5.3 Summary Statistics
  • 1.5.3.1 Analyzing the Summary Statistics for the Les Misérables Network
  • 1.6 Summary
  • Chapter 2 Subnetwork Analysis
  • 2.1 Introduction
  • 2.1.1 Isomorphism
  • 2.2 Connected Components
  • 2.2.1 Finding the Connected Components
  • 2.3 Biconnected Components
  • 2.3.1 Finding the Biconnected Components
  • 2.4 Community
  • 2.4.1 Finding Communities
  • 2.5 Core
  • 2.5.1 Finding k-Cores
  • 2.6 Reach Network
  • 2.6.1 Finding the Reach Network
  • 2.7 Network Projection
  • 2.7.1 Finding the Network Projection
  • 2.8 Node Similarity
  • 2.8.1 Computing Node Similarity
  • 2.9 Pattern Matching
  • 2.9.1 Searching for Subgraphs Matches
  • 2.10 Summary
  • Chapter 3 Network Centralities
  • 3.1 Introduction
  • 3.2 Network Metrics of Power and Influence
  • 3.3 Degree Centrality
  • 3.3.1 Computing Degree Centrality
  • 3.3.2 Visualizing a Network
  • 3.4 Influence Centrality
  • 3.4.1 Computing the Influence Centrality
  • 3.5 Clustering Coefficient
  • 3.5.1 Computing the Clustering Coefficient Centrality
  • 3.6 Closeness Centrality
  • 3.6.1 Computing the Closeness Centrality
  • 3.7 Betweenness Centrality
  • 3.7.1 Computing the Between Centrality
  • 3.8 Eigenvector Centrality.
  • 3.8.1 Computing the Eigenvector Centrality
  • 3.9 PageRank Centrality
  • 3.9.1 Computing the PageRank Centrality
  • 3.10 Hub and Authority
  • 3.10.1 Computing the Hub and Authority Centralities
  • 3.11 Network Centralities Calculation by Group
  • 3.11.1 By Group Network Centralities
  • 3.12 Summary
  • Chapter 4 Network Optimization
  • 4.1 Introduction
  • 4.1.1 History
  • 4.1.2 Network Optimization in SAS Viya
  • 4.2 Clique
  • 4.2.1 Finding Cliques
  • 4.3 Cycle
  • 4.3.1 Finding Cycles
  • 4.4 Linear Assignment
  • 4.4.1 Finding the Minimum Weight Matching in a Worker-Task Problem
  • 4.5 Minimum-Cost Network Flow
  • 4.5.1 Finding the Minimum-Cost Network Flow in a Demand-Supply Problem
  • 4.6 Maximum Network Flow Problem
  • 4.6.1 Finding the Maximum Network Flow in a Distribution Problem
  • 4.7 Minimum Cut
  • 4.7.1 Finding the Minimum Cuts
  • 4.8 Minimum Spanning Tree
  • 4.8.1 Finding the Minimum Spanning Tree
  • 4.9 Path
  • 4.9.1 Finding Paths
  • 4.10 Shortest Path
  • 4.10.1 Finding Shortest Paths
  • 4.11 Transitive Closure
  • 4.11.1 Finding the Transitive Closure
  • 4.12 Traveling Salesman Problem
  • 4.12.1 Finding the Optimal Tour
  • 4.13 Vehicle Routing Problem
  • 4.13.1 Finding the Optimal Vehicle Routes for a Delivery Problem
  • 4.14 Topological Sort
  • 4.14.1 Finding the Topological Sort in a Directed Graph
  • 4.15 Summary
  • Chapter 5 Real-World Applications in Network Science
  • 5.1 Introduction
  • 5.2 An Optimal Tour Considering a Multimodal Transportation System - The Traveling Salesman Problem Example in Paris
  • 5.3 An Optimal Beer Kegs Distribution - The Vehicle Routing Problem Example in Asheville
  • 5.4 Network Analysis and Supervised Machine Learning Models to Predict COVID-19 Outbreaks
  • 5.5 Urban Mobility in Metropolitan Cities.
  • 5.6 Fraud Detection in Auto Insurance Based on Network Analysis
  • 5.7 Customer Influence to Reduce Churn and Increase Product Adoption
  • 5.8 Community Detection to Identify Fraud Events in Telecommunications
  • 5.9 Summary
  • Index
  • EULA.

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