On uncertain graphs:
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, inf...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
[San Rafael, California]
Morgan & Claypool Publishers
[2018]
|
| Series: | Synthesis lectures on data management
#48 |
| Subjects: | |
| Online Access: | Volltext |
| Summary: | 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 |
| Item Description: | Part of: Synthesis digital library of engineering and computer science Title from PDF title page (viewed on August 1, 2018) |
| Physical Description: | 1 Online-Resource (xiii, 80 Seiten) Illustrationen |
| ISBN: | 9781681730387 |
| DOI: | 10.2200/S00862ED1V01Y201807DTM048 |
Staff View
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| 520 | |a 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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| spelling | Khan, Arijit Verfasser (DE-588)1169925545 aut On uncertain graphs Arijit Khan, Yuan Ye, Lei Chen [San Rafael, California] Morgan & Claypool Publishers [2018] © 2018 1 Online-Resource (xiii, 80 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Synthesis lectures on data management #48 Part of: Synthesis digital library of engineering and computer science Title from PDF title page (viewed on August 1, 2018) 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 Uncertainty (Information theory) Graphic methods Graphisches Modell (DE-588)4606156-3 gnd rswk-swf Netzwerk (DE-588)4171529-9 gnd rswk-swf Unvollkommene Information (DE-588)4140474-9 gnd rswk-swf Netzwerk (DE-588)4171529-9 s Graphisches Modell (DE-588)4606156-3 s Unvollkommene Information (DE-588)4140474-9 s DE-604 Ye, Yuan Verfasser aut Chen, Lei 1972- Verfasser (DE-588)140213279 aut Erscheint auch als Druck-Ausgabe, paperback 978-1-68173-037-0 Erscheint auch als Druck-Ausgabe, hardcover 978-1-68173-400-2 Synthesis lectures on data management #48 (DE-604)BV036731811 48 https://doi.org/10.2200/S00862ED1V01Y201807DTM048 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Khan, Arijit Ye, Yuan Chen, Lei 1972- On uncertain graphs Synthesis lectures on data management Uncertainty (Information theory) Graphic methods Graphisches Modell (DE-588)4606156-3 gnd Netzwerk (DE-588)4171529-9 gnd Unvollkommene Information (DE-588)4140474-9 gnd |
| subject_GND | (DE-588)4606156-3 (DE-588)4171529-9 (DE-588)4140474-9 |
| title | On uncertain graphs |
| title_auth | On uncertain graphs |
| title_exact_search | On uncertain graphs |
| title_full | On uncertain graphs Arijit Khan, Yuan Ye, Lei Chen |
| title_fullStr | On uncertain graphs Arijit Khan, Yuan Ye, Lei Chen |
| title_full_unstemmed | On uncertain graphs Arijit Khan, Yuan Ye, Lei Chen |
| title_short | On uncertain graphs |
| title_sort | on uncertain graphs |
| topic | Uncertainty (Information theory) Graphic methods Graphisches Modell (DE-588)4606156-3 gnd Netzwerk (DE-588)4171529-9 gnd Unvollkommene Information (DE-588)4140474-9 gnd |
| topic_facet | Uncertainty (Information theory) Graphic methods Graphisches Modell Netzwerk Unvollkommene Information |
| url | https://doi.org/10.2200/S00862ED1V01Y201807DTM048 |
| volume_link | (DE-604)BV036731811 |
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