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On Uncertain Graphs [electronic resource] / by Arijit Khan, Yuan Ye, Lei Chen.

By: Khan, Arijit [author.].
Contributor(s): Ye, Yuan [author.] | Chen, Lei [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Data Management: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XIII, 80 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031018602.Subject(s): Computer networks  | Data structures (Computer science) | Information theory | Computer Communication Networks | Data Structures and Information TheoryAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 004.6 Online resources: Click here to access online
Contents:
Acknowledgments -- Introduction to Uncertain Graphs -- Reliability Queries -- Graph Pattern Matching Queries -- Graph Similarity Search Queries -- Influence Maximization -- Major Open Problems -- Bibliography -- Authors' Biographies .
In: Springer Nature eBookSummary: 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.
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Acknowledgments -- Introduction to Uncertain Graphs -- Reliability Queries -- Graph Pattern Matching Queries -- Graph Similarity Search Queries -- Influence Maximization -- Major Open Problems -- Bibliography -- Authors' Biographies .

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.

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