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Record Nr. |
UNINA9910796096603321 |
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Autore |
Parsons A. F |
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Titolo |
Keynotes in organic chemistry / / Andrew F. Parsons |
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Pubbl/distr/stampa |
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Chichester, West Sussex : , : Wiley, , 2014 |
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ISBN |
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1-118-67642-4 |
1-118-67641-6 |
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Edizione |
[Second edition.] |
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Descrizione fisica |
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1 online resource (302 pages) : illustrations |
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Classificazione |
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Disciplina |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Includes bibliographical references (p. [249]-250) and index |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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2. |
Record Nr. |
UNINA9910831084303321 |
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Autore |
Reis Pinheiro Carlos Andre <1940-> |
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Titolo |
Network science : analysis and optimization algorithms for real-world applications / / Carlos Andre Reis Pinheiro |
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Pubbl/distr/stampa |
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Hoboken, New Jersey : , : Wiley, , [2023] |
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©2023 |
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ISBN |
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1-119-89894-3 |
1-119-89892-7 |
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Descrizione fisica |
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1 online resource (354 pages) |
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Disciplina |
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Soggetti |
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Computer networks |
Information networks |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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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 |
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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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Sommario/riassunto |
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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- |
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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. |
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