Mining Complex Networks
暫譯: 複雜網絡挖掘
Kaminski, Bogumil, Pralat, Pawel, Theberge, Francois
- 出版商: CRC
- 出版日期: 2021-12-15
- 售價: $4,110
- 貴賓價: 9.5 折 $3,905
- 語言: 英文
- 頁數: 264
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1032112034
- ISBN-13: 9781032112039
海外代購書籍(需單獨結帳)
商品描述
This book concentrates on mining networks, a subfield within data science. Data science uses scientific and computational tools to extract valuable knowledge from large data sets. Once data is processed and cleaned, it is analyzed and presented to support decision making processes. Data science and machine learning tools have become widely used in companies of all sizes.
Networks are often large-scale, decentralized, and evolve dynamically over time. Mining complex networks to understand the principles governing the organization and the behaviour of such networks is crucial for a broad range of fields of study. Here are a few selected typical applications of mining networks:
- Community detection (which users on some social media platform are close friends),
- Link prediction (who is likely to connect to whom on such platforms),
- Node attribute prediction (what advertisement should be shown to a given user of a particular platform to match their interests),
- Influential node detection (which social media users would be the best ambassadors of a specific product).
This textbook is suitable for an upper-year undergraduate course or a graduate course in programs such as data science, mathematics, computer science, business, engineering, physics, statistics, and social science. This book can be successfully used by all enthusiasts of data science at various levels of sophistication to expand their knowledge or consider changing their career path.
Jupiter notebooks (in Python and Julia) accompany the book and can be accessed on https: //www.ryerson.ca/mining-complex-networks/. These not only contain all of the experiments presented in the book yet also include additional material.
商品描述(中文翻譯)
這本書專注於網絡挖掘，這是數據科學的一個子領域。數據科學使用科學和計算工具從大型數據集提取有價值的知識。一旦數據被處理和清理，就會進行分析並呈現，以支持決策過程。數據科學和機器學習工具已在各種規模的公司中廣泛使用。
網絡通常是大規模的、去中心化的，並且隨著時間的推移而動態演變。挖掘複雜網絡以理解支配這些網絡組織和行為的原則，對於廣泛的研究領域至關重要。以下是一些網絡挖掘的典型應用：
- 社群檢測（在某些社交媒體平台上，哪些用戶是密友），
- 連結預測（誰可能會在這些平台上連接到誰），
- 節點屬性預測（應該向特定平台的給定用戶顯示什麼廣告以匹配他們的興趣），
- 影響力節點檢測（哪些社交媒體用戶將是特定產品的最佳大使）。
這本教科書適合高年級本科課程或研究生課程，適用於數據科學、數學、計算機科學、商業、工程、物理、統計和社會科學等專業。這本書可以成功地被各個層次的數據科學愛好者使用，以擴展他們的知識或考慮改變職業道路。
Jupiter notebooks（使用 Python 和 Julia）隨書附贈，並可在 https://www.ryerson.ca/mining-complex-networks/ 上訪問。這些不僅包含書中呈現的所有實驗，還包括額外的材料。
作者簡介
Bogumil Kamiński is the Chairman of the Scientific Council for the Discipline of Economics and Finance at SGH Warsaw School of Economics. He is also an Adjunct Professor at the Data Science Laboratory at Ryerson University. Bogumil is an expert in applications of mathematical modelling to solving complex real-life problems. He is also a substantial open-source contributor to the development of the Julia language and its package ecosystem.
Pawel Pralat is a Professor of Mathematics at Ryerson University, whose main research interests are in random graph theory, especially in modelling and mining complex networks. He is the Director of Fields-CQAM Lab on Computational Methods in Industrial Mathematics at The Fields Institute for Research in Mathematical Sciences and has pursued collaborations with various industry partners as well as the Government of Canada. He has written over 170 papers and 3 books with 130 plus collaborators.
François Théberge holds a B.Sc. degree in applied mathematics from the University of Ottawa, a M.Sc. in telecommunications from INRS and a PhD. in electrical engineering from McGill University. He has been employed by the Government of Canada since 1996 during which he was involved in the creation of the data science team as well as the research group now known as the Tutte Institute for Mathematics and Computing. He also holds an adjunct professorial position in the Department of Mathematics and Statistics at the University of Ottawa. His current interests include relational-data mining and deep learning.
作者簡介(中文翻譯)
Bogumil Kamiński 是華沙經濟學院經濟與金融學科科學委員會的主席。他同時也是瑞爾森大學數據科學實驗室的兼任教授。Bogumil 是數學建模應用於解決複雜現實問題的專家。他也是 Julia 語言及其套件生態系統的重要開源貢獻者。
Pawel Pralat 是瑞爾森大學的數學教授，他的主要研究興趣在於隨機圖理論，特別是在建模和挖掘複雜網絡方面。他是菲爾茲數學科學研究所的計算數學方法實驗室（Fields-CQAM Lab）的主任，並與多個行業夥伴及加拿大政府進行合作。他已發表超過 170 篇論文和 3 本書籍，與 130 多位合作者共同合作。
François Théberge 擁有渥太華大學應用數學的學士學位、INRS 的電信碩士學位以及麥吉爾大學的電機工程博士學位。自 1996 年以來，他一直在加拿大政府工作，期間參與了數據科學團隊的創建以及現在被稱為 Tutte 數學與計算研究所的研究小組。他同時在渥太華大學數學與統計系擔任兼任教授。他目前的研究興趣包括關聯數據挖掘和深度學習。
