Network Science

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.

• Preface xAcknowledgments xiiiAbout the Author xivAbout the Book xv1 Concepts in Network Science 11.1 Introduction 11.2 The Connector 21.3 History 31.3.1 A History in Social Studies 41.4 Concepts 51.4.1 Characteristics of Networks 71.4.2 Properties of Networks 71.4.3 Small World 81.4.4 Random Graphs 111.5 Network Analytics 121.5.1 Data Structure for Network Analysis and Network Optimization 131.5.1.1 Multilink and Self-Link 141.5.1.2 Loading and Unloading the Graph 151.5.2 Options for Network Analysis and Network Optimization Procedures 151.5.3 Summary Statistics 161.5.3.1 Analyzing the Summary Statistics for the Les Misérables Network 171.6 Summary 212 Subnetwork Analysis 232.1 Introduction 232.1.1 Isomorphism 252.2 Connected Components 262.2.1 Finding the Connected Components 272.3 Biconnected Components 352.3.1 Finding the Biconnected Components 362.4 Community 382.4.1 Finding Communities 452.5 Core 582.5.1 Finding k-Cores 592.6 Reach Network 622.6.1 Finding the Reach Network 652.7 Network Projection 702.7.1 Finding the Network Projection 722.8 Node Similarity 772.8.1 Computing Node Similarity 822.9 Pattern Matching 882.9.1 Searching for Subgraphs Matches 912.10 Summary 983 Network Centralities 1013.1 Introduction 1013.2 Network Metrics of Power and Influence 1023.3 Degree Centrality 1033.3.1 Computing Degree Centrality 1033.3.2 Visualizing a Network 1103.4 Influence Centrality 1143.4.1 Computing the Influence Centrality 1153.5 Clustering Coefficient 1213.5.1 Computing the Clustering Coefficient Centrality 1213.6 Closeness Centrality 1243.6.1 Computing the Closeness Centrality 1243.7 Betweenness Centrality 1293.7.1 Computing the Between Centrality 1303.8 Eigenvector Centrality 1363.8.1 Computing the Eigenvector Centrality 1373.9 PageRank Centrality 1443.9.1 Computing the PageRank Centrality 1443.10 Hub and Authority 1513.10.1 Computing the Hub and Authority Centralities 1523.11 Network Centralities Calculation by Group 1573.11.1 By Group Network Centralities 1583.12 Summary 1644 Network Optimization 1674.1 Introduction 1674.1.1 History 1674.1.2 Network Optimization in SAS Viya 1704.2 Clique 1704.2.1 Finding Cliques 1724.3 Cycle 1764.3.1 Finding Cycles 1774.4 Linear Assignment 1794.4.1 Finding the Minimum Weight Matching in a Worker-Task Problem 1814.5 Minimum-Cost Network Flow 1854.5.1 Finding the Minimum-Cost Network Flow in a Demand–Supply Problem 1884.6 Maximum Network Flow Problem 1944.6.1 Finding the Maximum Network Flow in a Distribution Problem 1954.7 Minimum Cut 1994.7.1 Finding the Minimum Cuts 2014.8 Minimum Spanning Tree 2054.8.1 Finding the Minimum Spanning Tree 2064.9 Path 2084.9.1 Finding Paths 2114.10 Shortest Path 2204.10.1 Finding Shortest Paths 2234.11 Transitive Closure 2354.11.1 Finding the Transitive Closure 2364.12 Traveling Salesman Problem 2394.12.1 Finding the Optimal Tour 2434.13 Vehicle Routing Problem 2494.13.1 Finding the Optimal Vehicle Routes for a Delivery Problem 2534.14 Topological Sort 2654.14.1 Finding the Topological Sort in a Directed Graph 2664.15 Summary 2685 Real-World Applications in Network Science 2715.1 Introduction 2715.2 An Optimal Tour Considering a Multimodal Transportation System – The Traveling Salesman Problem Example in Paris 2725.3 An Optimal Beer Kegs Distribution – The Vehicle Routing Problem Example in Asheville 2855.4 Network Analysis and Supervised Machine Learning Models to Predict COVID-19 Outbreaks 2985.5 Urban Mobility in Metropolitan Cities 3065.6 Fraud Detection in Auto Insurance Based on Network Analysis 3125.7 Customer Influence to Reduce Churn and Increase Product Adoption 3205.8 Community Detection to Identify Fraud Events in Telecommunications 3245.9 Summary 328Index 329