Social Networks with Rich Edge Semantics
暫譯: 具有豐富邊緣語義的社交網絡
Zheng, Quan, Skillicorn, David
- 出版商: CRC
- 出版日期: 2020-06-30
- 售價: $2,350
- 貴賓價: 9.5 折 $2,233
- 語言: 英文
- 頁數: 210
- 裝訂: Quality Paper - also called trade paper
- ISBN: 0367573253
- ISBN-13: 9780367573256
海外代購書籍(需單獨結帳)
商品描述
Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets.
Features
- Introduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over time
- Presents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzed
- Includes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriate
- Shows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a node
- Illustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groups
Suitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks.
商品描述(中文翻譯)
社交網絡與豐富邊緣語義介紹了一種新的機制，用於表示社交網絡，其中的成對關係可以從一系列現實的可能性中得出，包括不同類型的關係、成對方向的不同強度、正向和負向關係，以及隨時間變化的關係強度。對於每一種可能性，本書展示了如何使用光譜嵌入（spectral embedding）來建模社交網絡。它還展示了如何組合這些技術，以便能夠一起建模多種邊緣語義，並將建模技術應用於一系列數據集。
特點
- 向讀者介紹當前社交網絡分析的困難，以及對於節點之間關係的更豐富表示的需求，包括考慮強度、方向、類型、正向/負向以及隨時間變化的強度
- 提出一種新穎的機制，允許描述和分析具有質量上不同類型關係的社交網絡
- 包含對重要技術光譜嵌入的擴展，顯示這些擴展在數學上是有充分動機的，並證明其結果是合適的
- 展示如何利用嵌入來理解社交網絡中的結構，包括子群體、位置意義、鏈接或邊緣預測、在不同上下文中的角色一致性，以及通過節點的屬性淨流
- 說明該方法在現實世界問題中的應用，包括在線社交網絡、犯罪和毒品走私網絡，以及節點本身是群體的網絡
本書適合社交網絡研究、數據科學、統計學習及相關領域的研究人員和學生，將有助於提供對現實世界社交網絡的更深入理解。
作者簡介
David Skillicorn is a professor in the School of Computing at Queen's University. His undergraduate degree is from the University of Sydney and his Ph.D. from the University of Manitoba. He has published extensively in the area of adversarial data analytics, including his recent books "Understanding High-Dimensional Spaces" and "Knowledge Discovery for Counterterrorism and Law Enforcement". He has also been involved in interdisciplinary research on radicalisation, terrorism, and financial fraud. He consults for the intelligence and security arms of government in several countries, and appears frequently in the media to comment on cybersecurity and terrorism.
Dr. Quan Zheng got his Ph.D. is in the School of Computing from Queen's University in the year 2016.He has a Master's degree in Applied Mathematics with a specialization in statistics from Indiana University of Pennsylvania, and a Master's degree in Computer Science from the University of Ulm, and an undergraduate degree from Darmstadt University of Applied Science.
His research interests are in data mining and behavior analysis, particularly social network modeling and graph-based data analysis. He has proposed a few graph algorithms for identifying interested individuals and links, clustering and classification.
作者簡介(中文翻譯)
David Skillicorn 是女王大學計算學院的教授。他的學士學位來自悉尼大學，博士學位則來自曼尼托巴大學。他在對抗性數據分析領域發表了大量的研究，包括他最近出版的書籍《理解高維空間》（Understanding High-Dimensional Spaces）和《反恐與執法的知識發現》（Knowledge Discovery for Counterterrorism and Law Enforcement）。他還參與了有關激進化、恐怖主義和金融詐騙的跨學科研究。他為幾個國家的情報和安全機構提供諮詢，並經常在媒體上就網絡安全和恐怖主義發表評論。
Dr. Quan Zheng 於2016年在女王大學計算學院獲得博士學位。他擁有賓夕法尼亞州印第安納大學的應用數學碩士學位，專攻統計學，以及烏爾姆大學的計算機科學碩士學位，並在達姆施塔特應用科技大學獲得學士學位。
他的研究興趣包括數據挖掘和行為分析，特別是社交網絡建模和基於圖形的數據分析。他提出了一些圖形算法，用於識別感興趣的個體和連結、聚類和分類。
