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E-grāmata: Network Science - Analysis and Optimization Algorithms for Real-World Applications: Analysis and Optimization Algorithms for Real-World Applications [Wiley Online]

  • Formāts: 352 pages
  • Izdošanas datums: 24-Nov-2022
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119898943
  • ISBN-13: 9781119898948
  • Wiley Online
  • Cena: 100,83 €*
  • * this price gives unlimited concurrent access for unlimited time
  • Formāts: 352 pages
  • Izdošanas datums: 24-Nov-2022
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119898943
  • ISBN-13: 9781119898948
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 network analysis, ensuring a smooth learning experience for readers. It also includes a detailed introduction to multiple network optimization algorithms, including linear assignment, network flow and routing problems.

The text is comprised of five chapters, focusing on subgraphs, network analysis, network optimization, and includes a list of case studies, those of which include influence factors in telecommunications, fraud detection in taxpayers, identifying the viral effect in purchasing, finding optimal routes considering public transportation systems, among many others. This insightful book shows how to apply algorithms to solve complex problems in real-life scenarios and shows the math behind these algorithms, enabling readers to learn how to develop them and scrutinize the results.

Written by a highly qualified author with significant experience in the field, Network Science also includes information on:

  • Sub-networks, covering connected components, bi-connected components, community detection, k-core decomposition, reach network, projection, nodes similarity and pattern matching
  • Network centrality measures, covering degree, influence, clustering coefficient, closeness, betweenness, eigenvector, PageRank, hub and authority
  • Network optimization, covering clique, cycle, linear assignment, minimum-cost network flow, maximum network flow problem, minimum cut, minimum spanning tree, path, shortest path, transitive closure, traveling salesman problem, vehicle routing problem and topological sort

With in-depth and authoritative coverage of the subject and many case studies to convey concepts clearly, Network Science is a helpful training resource for professional and industry workers in, telecommunications, insurance, retail, banking, healthcare, public sector, among others, plus as a supplementary reading for an introductory Network Science course for undergraduate students.

Preface x
Acknowledgments xiii
About the Author xiv
About the Book xv
1 Concepts in Network Science
1(22)
1.1 Introduction
1(1)
1.2 The Connector
2(1)
1.3 History
3(2)
1.3.1 A History in Social Studies
4(1)
1.4 Concepts
5(7)
1.4.1 Characteristics of Networks
7(1)
1.4.2 Properties of Networks
7(1)
1.4.3 Small World
8(3)
1.4.4 Random Graphs
11(1)
1.5 Network Analytics
12(9)
1.5.1 Data Structure for Network Analysis and Network Optimization
13(1)
1.5.1.1 Multilink and Self-Link
14(1)
1.5.1.2 Loading and Unloading the Graph
15(1)
1.5.2 Options for Network Analysis and Network Optimization Procedures
15(1)
1.5.3 Summary Statistics
16(1)
1.5.3.1 Analyzing the Summary Statistics for the Les Miserables Network
17(4)
1.6 Summary
21(2)
2 Subnetwork Analysis
23(78)
2.1 Introduction
23(3)
2.1.1 Isomorphism
25(1)
2.2 Connected Components
26(9)
2.2.1 Finding the Connected Components
27(8)
2.3 Biconnected Components
35(3)
2.3.1 Finding the Biconnected Components
36(2)
2.4 Community
38(20)
2.4.1 Finding Communities
45(13)
2.5 Core
58(4)
2.5.1 Finding k-Cores
59(3)
2.6 Reach Network
62(8)
2.6.1 Finding the Reach Network
65(5)
2.7 Network Projection
70(7)
2.7.1 Finding the Network Projection
72(5)
2.8 Node Similarity
77(11)
2.8.1 Computing Node Similarity
82(6)
2.9 Pattern Matching
88(10)
2.9.1 Searching for Subgraphs Matches
91(7)
2.10 Summary
98(3)
3 Network Centralities
101(66)
3.1 Introduction
101(1)
3.2 Network Metrics of Power and Influence
102(1)
3.3 Degree Centrality
103(11)
3.3.1 Computing Degree Centrality
103(7)
3.3.2 Visualizing a Network
110(4)
3.4 Influence Centrality
114(7)
3.4.1 Computing the Influence Centrality
115(6)
3.5 Clustering Coefficient
121(3)
3.5.1 Computing the Clustering Coefficient Centrality
121(3)
3.6 Closeness Centrality
124(5)
3.6.1 Computing the Closeness Centrality
124(5)
3.7 Betweenness Centrality
129(7)
3.7.1 Computing the Between Centrality
130(6)
3.8 Eigenvector Centrality
136(8)
3.8.1 Computing the Eigenvector Centrality
137(7)
3.9 PageRank Centrality
144(7)
3.9.1 Computing the PageRank Centrality
144(7)
3.10 Hub and Authority
151(6)
3.10.1 Computing the Hub and Authority Centralities
152(5)
3.11 Network Centralities Calculation by Group
157(7)
3.11.1 By Group Network Centralities
158(6)
3.12 Summary
164(3)
4 Network Optimization
167(104)
4.1 Introduction
167(3)
4.1.1 History
167(3)
4.1.2 Network Optimization in SAS Viya
170(1)
4.2 Clique
170(6)
4.2.1 Finding Cliques
172(4)
4.3 Cycle
176(3)
4.3.1 Finding Cycles
177(2)
4.4 Linear Assignment
179(6)
4.4.1 Finding the Minimum Weight Matching in a Worker-Task Problem
181(4)
4.5 Minimum-Cost Network Flow
185(9)
4.5.1 Finding the Minimum-Cost Network Flow in a Demand-Supply Problem
188(6)
4.6 Maximum Network Flow Problem
194(5)
4.6.1 Finding the Maximum Network Flow in a Distribution Problem
195(4)
4.7 Minimum Cut
199(6)
4.7.1 Finding the Minimum Cuts
201(4)
4.8 Minimum Spanning Tree
205(3)
4.8.1 Finding the Minimum Spanning Tree
206(2)
4.9 Path
208(12)
4.9.1 Finding Paths
211(9)
4.10 Shortest Path
220(15)
4.10.1 Finding Shortest Paths
223(12)
4.11 Transitive Closure
235(4)
4.11.1 Finding the Transitive Closure
236(3)
4.12 Traveling Salesman Problem
239(10)
4.12.1 Finding the Optimal Tour
243(6)
4.13 Vehicle Routing Problem
249(16)
4.13.1 Finding the Optimal Vehicle Routes for a Delivery Problem
253(12)
4.14 Topological Sort
265(3)
4.14.1 Finding the Topological Sort in a Directed Graph
266(2)
4.15 Summary
268(3)
5 Real-World Applications in Network Science
271(58)
5.1 Introduction
271(1)
5.2 An Optimal Tour Considering a Multimodal Transportation System - The Traveling Salesman Problem Example in Paris
272(13)
5.3 An Optimal Beer Kegs Distribution - The Vehicle Routing Problem Example in Asheville
285(13)
5.4 Network Analysis and Supervised Machine Learning Models to Predict COVID-19 Outbreaks
298(8)
5.5 Urban Mobility in Metropolitan Cities
306(6)
5.6 Fraud Detection in Auto Insurance Based on Network Analysis
312(8)
5.7 Customer Influence to Reduce Churn and Increase Product Adoption
320(4)
5.8 Community Detection to Identify Fraud Events in Telecommunications
324(4)
5.9 Summary
328(1)
Index 329
Carlos Andre Reis Pinheiro is a Distinguished Data Scientist at SAS, USA. Dr. Pinheiro received his DSc in Engineering from the Federal University of Rio de Janeiro and has published several papers in international journals and conferences. He is the author of Heuristics in Analytics and Social Network Analysis in Telecommunications, both published by Wiley.