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

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, like minimum-cost network flow, maximum network flow, linear assignment, traveling salesman and vehicle 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.

《網絡科學》提供了關於網絡分析和網絡優化算法的全面見解，並通過簡單的逐步指南和示例，以及對網絡科學的詳細介紹和歷史，解釋了關鍵概念和進行網絡分析所需的數據類型，確保讀者能夠順利學習。它還包括對多種網絡優化算法的詳細介紹，如最小成本網絡流、最大網絡流、線性分配、旅行推銷員和車輛路線問題。

本書共分為五章，重點介紹子圖、網絡分析、網絡優化，並列舉了一系列案例研究，包括電信中的影響因素、納稅人欺詐檢測、購買中的病毒效應、考慮公共交通系統的最優路線等。這本富有洞察力的書展示了如何應用算法解決現實生活中的複雜問題，並展示了這些算法背後的數學原理，使讀者能夠學習如何開發它們並審查結果。

本書由一位在該領域具有豐富經驗的高資歷作者撰寫，還包括以下信息：

- 子網絡，包括連通分量、雙連通分量、社區檢測、k-核分解、可達網絡、投影、節點相似性和模式匹配
- 網絡中心性度量，包括度、影響力、聚集係數、接近度、中介度、特徵向量、PageRank、中心節點和權威節點
- 網絡優化，包括團、環、線性分配、最小成本網絡流、最大網絡流問題、最小割、最小生成樹、路徑、最短路徑、傳遞閉包、旅行推銷員問題、車輛路線問題和拓撲排序

《網絡科學》深入而權威地涵蓋了這一主題，並通過許多案例研究清晰地傳達概念，是電信、保險、零售、銀行、醫療保健、公共部門等專業和行業工作者的有用培訓資源，也可作為本科生網絡科學入門課程的補充閱讀材料。

Carlos 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.

Carlos Pinheiro是SAS公司美國分部的傑出數據科學家。Pinheiro博士畢業於巴西里約熱內盧聯邦大學，並在國際期刊和會議上發表了多篇論文。他是Wiley出版社的《Heuristics in Analytics》和《Social Network Analysis in Telecommunications》的作者。