Heterogeneous Graph Representation Learning and Applications
暫譯: 異質圖表示學習及其應用

Shi, Chuan, Wang, Xiao, Yu, Philip S.

Heterogeneous Graph Representation Learning and Applications
  • 出版商: Springer
  • 出版日期: 2023-02-01
  • 售價: $7,120
  • 貴賓價: 9.5 折 $6,764
  • 語言: 英文
  • 頁數: 318
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9811661685
  • ISBN-13: 9789811661686
  • 相關分類: 大數據 Big-data、Machine Learning、DeepLearning

    • 海外代購書籍(需單獨結帳)

商品描述

Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node.
In this book, we provide a comprehensive survey of current developments in HG representation learning. More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.


商品描述(中文翻譯)

在異質圖（HG）中的表示學習旨在為每個節點提供有意義的向量表示，以促進下游應用，如連結預測、個性化推薦、節點分類等。然而，這項任務具有挑戰性，不僅因為需要整合由多種類型的節點和邊組成的異質結構（圖）信息，還需要考慮與每個節點相關的異質屬性或內容類型（例如文本或圖像）。儘管在同質（和異質）圖嵌入、屬性圖嵌入和圖神經網絡方面已取得相當大的進展，但能夠同時有效考慮異質結構（圖）信息以及每個節點的異質內容信息的研究仍然很少。

在本書中，我們提供了對異質圖表示學習當前發展的全面調查。更重要的是，我們展示了該領域的最新技術，包括在頂級會議和期刊（如TKDE、KDD、WWW、IJCAI和AAAI）上展示的理論模型和實際應用。本書有兩個主要目標：（1）為研究人員提供對基本問題的理解，並為在這個快速擴展的領域中工作提供良好的起點；（2）展示最新的研究，應用異質圖來建模真實系統並學習互動系統的結構特徵。據我們所知，這是第一本總結最新發展並展示異質圖表示學習前沿研究的書籍。為了從中獲益，讀者應具備計算機科學、數據挖掘和機器學習的基本知識。

作者簡介

Chuan Shi is the professor in School of Computer Sciences of Beijing University of Posts and Telecommunications, deputy director of Beijing Key Lab of Intelligent Telecommunication Software and Multimedia. The main research interests include data mining, machine learning, artificial intelligence and big data analysis. He has published more than 100 refereed papers, including top journals and conferences in data mining, such as IEEE TKDE, ACM TIST, KDD, AAAI, IJCAI, and WWW. And in the meanwhile, his first monograph about heterogeneous information networks has been published by Springer. He has been honored as the best paper award in ADMA 2011 and ADMA 2018, and has guided students to the world champion in the IJCAI Contest 2015, the premier international data mining competition. He is also the recipient of "the Youth Talent Plan" and "the Pioneer of Teacher's Ethics" in Beijing.

Xiao Wang is the assistant professor in School of Computer Sciences of Beijing University of Posts and Telecommunications. He was a postdoc in the Department of Computer Science and Technology at Tsinghua University. He got his Ph.D. in the School of Computer Science and Technology at Tianjin University and a joint-training Ph.D. at Washington University in St. Louis. The main research interests include data mining, machine learning, artificial intelligence and big data analysis. He has published more than 50 refereed papers, including top journals and conferences in data mining, such as IEEE TKDE, KDD, AAAI, IJCAI, and WWW. He also serves as SPC/PC member and Reviewer of several high-level international conferences, e.g., KDD, AAAI, IJCAI, and journals, e.g., IEEE TKDE.

Philip S. Yu's main research interests include big data, data mining (especially on graph/network mining), social network, privacy preserving data publishing, data stream, database systems, and Internet applications and technologies. He is a Distinguished Professor in the Department of Computer Science at UIC and also holds the Wexler Chair in Information and Technology. Before joining UIC, he was with IBM Thomas J. Watson Research Center, where he was manager of the Software Tools and Techniques department. Dr. Yu has published more than 1,300 papers in refereed journals and conferences with more than 133,000 citations and an H-index of 169. He holds or has applied for more than 300 US patents. Dr. Yu is a Fellow of the ACM and the IEEE. He is the recepient of ACM SIGKDD 2016 Innovation Award and the IEEE Computer Society's 2013 Technical Achievement Award.

作者簡介(中文翻譯)

傳世是北京郵電大學計算機科學學院的教授，北京市智能電信軟體與多媒體重點實驗室的副主任。主要研究興趣包括資料探勘、機器學習、人工智慧和大數據分析。他已發表超過100篇經過審核的論文，包括資料探勘領域的頂級期刊和會議，如IEEE TKDE、ACM TIST、KDD、AAAI、IJCAI和WWW。同時，他的第一本專著《異質信息網絡》已由Springer出版。他曾在ADMA 2011和ADMA 2018獲得最佳論文獎，並指導學生在2015年IJCAI比賽中獲得世界冠軍，該比賽是國際頂尖的資料探勘競賽。他還獲得了北京市的「青年人才計畫」和「教師倫理先鋒」的榮譽。

小王是北京郵電大學計算機科學學院的助理教授。他曾在清華大學計算機科學與技術系擔任博士後研究員。他在天津大學計算機科學與技術學院獲得博士學位，並在聖路易斯華盛頓大學進行聯合培訓的博士學位。主要研究興趣包括資料探勘、機器學習、人工智慧和大數據分析。他已發表超過50篇經過審核的論文，包括資料探勘領域的頂級期刊和會議，如IEEE TKDE、KDD、AAAI、IJCAI和WWW。他還擔任多個高水平國際會議的SPC/PC成員和審稿人，例如KDD、AAAI、IJCAI，以及期刊如IEEE TKDE。

Philip S. Yu的主要研究興趣包括大數據、資料探勘（特別是圖形/網絡探勘）、社交網絡、隱私保護資料發布、資料流、資料庫系統以及互聯網應用和技術。他是UIC計算機科學系的特聘教授，並擔任資訊與技術的Wexler講座教授。在加入UIC之前，他曾在IBM Thomas J. Watson研究中心工作，擔任軟體工具與技術部門的經理。Yu博士已在經過審核的期刊和會議上發表超過1,300篇論文，引用次數超過133,000次，H指數為169。他持有或已申請超過300項美國專利。Yu博士是ACM和IEEE的會士，並獲得了ACM SIGKDD 2016創新獎和IEEE計算機學會2013技術成就獎。

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