Cohesive Subgraph Search Over Large Heterogeneous Information Networks
暫譯: 大型異質資訊網絡中的凝聚子圖搜尋

Fang, Yixiang, Wang, Kai, Lin, Xuemin

Cohesive Subgraph Search Over Large Heterogeneous Information Networks
  • 出版商: Springer
  • 出版日期: 2022-05-07
  • 售價: $2,080
  • 貴賓價: 9.5 折 $1,976
  • 語言: 英文
  • 頁數: 74
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3030975673
  • ISBN-13: 9783030975678

    • 海外代購書籍(需單獨結帳)

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商品描述

This SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs). It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs.

The authors first classify the existing works of CSS over HINs according to the classic cohesiveness metrics such as core, truss, clique, connectivity, density, etc., and then extensively review the specific models and their corresponding search solutions in each group. Note that since the bipartite network is a special case of HINs, all the models developed for general HINs can be directly applied to bipartite networks, but the models customized for bipartite networks may not be easily extended for other general HINs due to their restricted settings. The authors also analyze and compare these cohesive subgraph models (CSMs) and solutions systematically. Specifically, the authors compare different groups of CSMs and analyze both their similarities and differences, from multiple perspectives such as cohesiveness constraints, shared properties, and computational efficiency. Then, for the CSMs in each group, the authors further analyze and compare their model properties and high-level algorithm ideas.

This SpringerBrief targets researchers, professors, engineers and graduate students, who are working in the areas of graph data management and graph mining. Undergraduate students who are majoring in computer science, databases, data and knowledge engineering, and data science will also want to read this SpringerBrief.

商品描述(中文翻譯)

這本SpringerBrief提供了對於大型異質資訊網路（HINs）中凝聚子圖搜尋（CSS）現有工作的首次系統性回顧。它還涵蓋了該領域的研究突破，包括近年來的模型、演算法和比較研究。這本SpringerBrief提供了一系列在大型HINs上進行CSS的未來研究方向的建議。

作者首先根據經典的凝聚性指標（如核心、桁架、團、連通性、密度等）對HINs上的現有CSS工作進行分類，然後廣泛回顧每個組別中的具體模型及其相應的搜尋解決方案。需要注意的是，由於二部網路是HINs的一個特例，為一般HINs開發的所有模型都可以直接應用於二部網路，但針對二部網路定制的模型可能因其限制性設置而不易擴展到其他一般HINs。作者還系統性地分析和比較這些凝聚子圖模型（CSMs）及其解決方案。具體而言，作者比較不同組別的CSMs，並從凝聚性約束、共享屬性和計算效率等多個角度分析它們的相似性和差異。然後，對於每個組別中的CSMs，作者進一步分析和比較它們的模型屬性和高層次的演算法思路。

這本SpringerBrief的目標讀者是從事圖形數據管理和圖形挖掘領域的研究人員、教授、工程師和研究生。主修計算機科學、數據庫、數據與知識工程以及數據科學的本科生也會希望閱讀這本SpringerBrief。

作者簡介

Yixiang Fang is an Associate Professor in the School of Data Science, The Chinese University of Hong Kong, Shenzhen. He received Ph.D. in computer science from the University of Hong Kong in 2017. After that, he worked as a Research Associate in the School of Computer Science and Engineering, University of New South Wales, with Prof. Xuemin Lin. His research interests include querying, mining, and analytics of big graph data and big spatial data. He has published extensively in the areas of database and data mining, and most of his papers were published in top-tier conferences (e.g., PVLDB, SIGMOD, ICDE, NeurIPS, and IJCAI) and journals (e.g., TODS, VLDBJ, and TKDE), including One of the Best Papers in SIGMOD 2020. He received the 2021 ACM SIGMOD Research Highlight Award. He is an editorial board member of the journal of Information \& Processing Management (IPM). He has also served as program committee members for several top conferences (e.g., ICDE, KDD, AAAI, and IJCAI) and invited reviewers for top journals (e.g., TKDE, VLDBJ, and TOC) in the areas of database and data mining.
Kai Wang is a Research Associate in the School of Computer Science and Engineering, the University of New South Wales, Australia. He received the BSc degree from Zhejiang University in 2016 and the Ph.D. degree from the University of New South Wales in 2020, both in computer science. His research interests lie in big data analytics, especially for the big graph and spatial data. Most of his research works were published in top-tier database conferences (e.g., SIGMOD, PVLDB, and ICDE) and journals (e.g., VLDBJ and TKDE).
Xuemin Lin is a UNSW Scientia Professor in the School of Computer Science and Engineering at the University of New South Wales, Australia. He is a Fellow of IEEE. He received the BSc degree in applied math from Fudan University in 1984, and the Ph.D. degree in computer science from the University of Queensland in 1992. Currently, he is the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering. His principal research areas are databases and graph visualization.
Wenjie Zhang is a Professor and ARC Future Fellow in the School of Computer Science and Engineering, the University of New South Wales, Australia. She received Ph.D. from the University of New South Wales in 2010. She is an Associate Editor of IEEE Transactions on Knowledge and Data Engineering. Her research interests lie in large-scale data processing especially in query processing over spatial and graph/network data.

作者簡介(中文翻譯)

方怡翔是香港中文大學（深圳）數據科學學院的副教授。他於2017年在香港大學獲得計算機科學博士學位。之後，他在新南威爾士大學計算機科學與工程學院擔任研究助理，與林學敏教授合作。他的研究興趣包括大圖數據和大空間數據的查詢、挖掘和分析。他在數據庫和數據挖掘領域發表了大量論文，其中大多數論文發表在頂級會議（如PVLDB、SIGMOD、ICDE、NeurIPS和IJCAI）和期刊（如TODS、VLDBJ和TKDE）上，包括2020年SIGMOD最佳論文之一。他獲得了2021年ACM SIGMOD研究亮點獎。他是《信息與處理管理》（IPM）期刊的編輯委員會成員。他還擔任過多個頂級會議（如ICDE、KDD、AAAI和IJCAI）的程序委員會成員，以及數據庫和數據挖掘領域頂級期刊（如TKDE、VLDBJ和TOC）的邀請審稿人。
王凱是新南威爾士大學計算機科學與工程學院的研究助理。他於2016年在浙江大學獲得學士學位，並於2020年在新南威爾士大學獲得計算機科學博士學位。他的研究興趣在於大數據分析，特別是大圖和空間數據的分析。他的大多數研究工作發表在頂級數據庫會議（如SIGMOD、PVLDB和ICDE）和期刊（如VLDBJ和TKDE）上。
林學敏是新南威爾士大學計算機科學與工程學院的Scientia教授。他是IEEE的會士。他於1984年在復旦大學獲得應用數學學士學位，並於1992年在昆士蘭大學獲得計算機科學博士學位。目前，他是IEEE知識與數據工程學報的主編。他的主要研究領域是數據庫和圖形可視化。
張文杰是新南威爾士大學計算機科學與工程學院的教授和ARC未來研究員。她於2010年在新南威爾士大學獲得博士學位。她是IEEE知識與數據工程學報的副編輯。她的研究興趣在於大規模數據處理，特別是在空間和圖形/網絡數據的查詢處理方面。

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