Mining Heterogeneous Information Networks 在線電子書 圖書標籤: 數據挖掘 Networks Heterogeneous mining; Information 異質網絡 data 計算機
發表於2024-11-25
Mining Heterogeneous Information Networks 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024
meta path 讓我想到瞭前段時間老師讓看的引文分析裏麵的“主路徑分析法”,both interesting
評分韓老師頗為推薦的書,大約因為作者是他的愛徒吧。data mining的書都是看起來有道理,但感覺不算非常solid。。。去年KDD就有人吐槽allmodels are wrong, some are useful。。。
評分今年Heterogeneous Network相關工作明顯火瞭,阿裏推薦的場景比GBDT+Metapth2vec高瞭2.3%。但總覺得一味GNN不是辦法,看看以前的工作還是好的。
評分韓老師頗為推薦的書,大約因為作者是他的愛徒吧。data mining的書都是看起來有道理,但感覺不算非常solid。。。去年KDD就有人吐槽allmodels are wrong, some are useful。。。
評分meta path 讓我想到瞭前段時間老師讓看的引文分析裏麵的“主路徑分析法”,both interesting
Yizhou Sun received her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2012. She will be an assistant professor in the College of Computer and Information Science at Northeastern University. Her principal research interest is in mining information and social networks, and more generally in data mining, database systems, statistics, machine learning, information retrieval, and network science, with a focus on modeling novel problems and proposing scalable algorithms for large-scale, real world applications. Yizhou has over 30 publications in books, journals, and major conferences. Tutorials based on her thesis work on mining heterogeneous information networks have been given in several premier conferences, including EDBT 2009, SIGMOD 2010, KDD 2010, ICDE 2012, VLDB 2012, and ASONAM 2012. She received ACM KDD 2012 Best Student Paper Award.
Jiawei Han is the Abel Bliss Professor of Computer Science at the University of Illinois at Urbana-Champaign. His research includes data mining, information network analysis, database sys-tems, and data warehousing, with over 600 journal and confer-ence publications. He has chaired or served on many program committees of international conferences, including PC co-chair for KDD, SDM, and ICDM conferences, and Americas Coor-dinator for VLDB conferences. He also served as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data and is serving as the Director of Information Network Academic Research Center supported by U.S. Army Research Lab. He is Fellow of ACM and Fellow of IEEE, and received 2004 ACM SIGKDD Innovations Award, 2005 IEEE Computer Society Technical Achievement Award, 2009 IEEE Computer So-ciety Wallace McDowell Award, and 2011 Daniel C. Drucker Eminent Faculty Award at UIUC. His book,Data Mining: Concepts and Techniques, has been used popularly as a textbook worldwide.
Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge.
In this book, we investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including: (1) rank-based clustering and classification; (2) meta-path-based similarity search and mining; (3) relation strength-aware mining, and many other potential developments. This book introduces this new research frontier and points out some promising research directions.
Table of Contents: Introduction / Ranking-Based Clustering / Classification of Heterogeneous Information Networks / Meta-Path-Based Similarity Search / Meta-Path-Based Relationship Prediction / Relation Strength-Aware Clustering with Incomplete Attributes / User-Guided Clustering via Meta-Path Selection / Research Frontiers
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Mining Heterogeneous Information Networks 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024