Heterogeneous Graph Representation Learning and Applications

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.

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，以便促進下游應用，如連結預測、個性化推薦、節點分類等。然而，這個任務具有挑戰性，不僅因為需要融合包含多種節點和邊緣類型的異質結構（圖）信息，還因為需要考慮與每個節點關聯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了相當大的進展，但很少有能夠同時有效地考慮異質結構（圖）信息以及每個節點的異質內容信息的方法。

在本書中，我們對當前異質圖表示學習的發展進行了全面的調查。更重要的是，我們介紹了這一領域的最新技術，包括在頂級會議和期刊（如TKDE、KDD、WWW、IJCAI和AAAI）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和在這個快速發展領域中開展工作的良好起點，以及（2）介紹將異質圖應用於建模真實系統和學習交互系統結構特徵的最新研究。據我們所知，這是第一本總結最新發展並介紹異質圖表示學習前沿研究的書籍。讀者應該具備基本的計算機科學、數據挖掘和機器學習知識，以充分受益於本書。

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.

川石是北京郵電大學計算機科學學院的教授，北京智能通信軟件與多媒體重點實驗室的副主任。他的主要研究方向包括數據挖掘、機器學習、人工智能和大數據分析。他發表了100多篇經過同行評審的論文，包括在數據挖掘領域的頂級期刊和會議，如IEEE TKDE、ACM TIST、KDD、AAAI、IJCAI和WWW。同時，他的第一本關於異構信息網絡的專著已由Springer出版。他曾獲得2011年和2018年ADMA的最佳論文獎，並指導學生在2015年IJCAI競賽中獲得世界冠軍，這是首屈一指的國際數據挖掘競賽。他還獲得了北京市“青年英才計劃”和“師德先鋒”稱號。

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做博士後研究，並在天津大學計算機科學與技術學院獲得博士學位，並在華盛頓大學聯合培養博士學位。他的主要研究方向包括數據挖掘、機器學習、人工智能和大數據分析。他發表了50多篇經過同行評審的論文，包括在數據挖掘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（如KDD、AAAI、IJCAI）和期刊（如IEEE TKDE）的SPC/PC成員和評審。

Philip S. Yu的主要研究方向包括大數據、數據挖掘（尤其是圖形/網絡挖掘）、社交網絡、隱私保護數據發布、數據流、數據庫系統和互聯網應用和技術。他是UIC計算機科學系的傑出教授，並擔任Wexler信息與技術講座。在加入UIC之前，他曾在IBM Thomas J. Watson研究中心擔任軟件工具和技術部門的經理。Yu博士在同行評審的期刊和會議上發表了1300多篇論文，被引用超過133,000次，H指數為169。他持有或申請了300多項美國專利。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年技術成就獎。