Network Science. Analysis and Optimization Algorithms for Real-World Applications

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
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