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Large-scale, highly interconnected networks, which are often modeled as graphs, pervade both our society and the natural world around us. Uncertainty, on the other hand, is inherent in the underlying data due to a variety of reasons, such as noisy measurements, lack of precise information needs, inference and prediction models, or explicit manipulation, e.g., for privacy purposes. Therefore, uncertain, or probabilistic, graphs are increasingly used to represent noisy linked data in many emerging application scenarios, and they have recently become a hot topic in the database and data mining communities. Many classical algorithms such as reachability and shortest path queries become #P-complete and, thus, more expensive over uncertain graphs. Moreover, various complex queries and analytics are also emerging over uncertain networks, such as pattern matching, information diffusion, and influence maximization queries. In this book, we discuss the sources of uncertain graphs and their applications, uncertainty modeling, as well as the complexities and algorithmic advances on uncertain graphs processing in the context of both classical and emerging graph queries and analytics. We emphasize the current challenges and highlight some future research directions.
Acerca de Lei Chen
Lei Chen received a B.S. in Computer Science and Engineering from Tianjin University, China in 1994, an M.A. from Asian Institute of Technology, Thailand, in 1997, and a Ph.D. in Computer Science from University of Waterloo, Canada in 2005. He is now an associate professor in the Department of Computer Science and Engineering at Hong Kong University of Science and Technology. His research interests include uncertain databases, graph databases, multime80 AUTHORS’ BIOGRAPHIES dia, and time series databases, and sensor and peer-to-peer databases. He is editor-in-chief of the VLDB Journal and serving as an associate editor for IEEE Transactions on Knowledge and Data Engineering and Distributed and Parallel Databases. He is the PC Co-chair of the 45th International Conference on Very Large Databases (VLDB), 2019, and has served as PC Cochair, PC Track Chair, and PC member for many conferences. He was awarded the SIGMOD Test of Time Award in 2015. He is a member of the IEEE and ACM.
Acerca de Arijit Khan
Arijit Khan is an assistant professor in the School of Computer Engineering at Nanyang Technological University, Singapore. His research interests span in the area of big-data, big-graphs, and graph systems. He received his Ph.D. from the Department of Computer Science, University of California, Santa Barbara, and did a post-doc in the Systems group at ETH Zurich. Arijit was the recipient of the prestigious IBM Ph.D. Fellowship in 2012-13. He published several papers in premier database and data-mining conferences and journals including SIGMOD, VLDB, TKDE, ICDE, SDM, EDBT, and CIKM. Arijit co-presented tutorials on emerging graph queries, big-graph systems, summarization, and uncertain graphs at ICDE 2012, VLDB 2014, VLDB 2015, and VLDB 2017, and served in the program committee of KDD, SIGMOD, VLDB, ICDM, EDBT, WWW, and CIKM. Arijit served as the co-chair of Big-O(Q) workshop co-located with VLDB 2015.
Acerca de Yuan Ye
Yuan Ye is now a professor in the Department of Computer Science, Northeastern University, China. His research interests are graph databases, probabilistic databases, social network analysis and big-data computing systems. Yuan Ye received the B.S., M.S., and Ph.D. degrees in Computer Science from Northeastern University in 2004, 2007, and 2011, respectively. He was a visiting scholar of the Hong Kong University of Science and Technology, the Chinese University of Hong Kong, and the University of Edinburgh. Yuan Ye published several papers in premier database conferences and journals including SIGMOD, VLDB, ICDE, CIKM, VLDB Journal, TKDE, and TPDS. He served in the program committee of SIGMOD, VLDB, ICDE, EDBT, and CIKM. Yuan Ye received the award of the CCF excellent doctoral dissertation in 2012 and the excellent youth scholar of NSFC in 2016.
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