Pattern Match Query in a Large Uncertain Graph

Yuan, Ye; Wang, Guoren; Chen, Lei

doi:10.1145/2661829.2661868

Cited by 9 publications

(4 citation statements)

References 38 publications

(52 reference statements)

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“…This method is tested with both synthetic as well as real-world datasets. In a follow up work by yuan et al [122] [124], they developed probabilistic match trees (PM Trees) based on match cuts and cut selection process. Considering this index structure, they developed effective pruning strategy to prune the unqualified matches.…”

Section: Han Et Al [45]mentioning

confidence: 99%

A Survey on Mining and Analysis of Uncertain Graphs

Banerjee¹

2021

Preprint

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Uncertain Graph (also known as Probabilistic Graph) is a generic model to represent many real-world networks from social to biological. In recent times analysis and mining of uncertain graphs have drawn significant attention from the researchers of the data management community. Several noble problems have been introduced and efficient methodologies have been developed to solve those problems. Hence, there is a need to summarize the existing results on this topic in a self-organized way. In this paper, we present a comprehensive survey on uncertain graph mining focusing on mainly three aspects: (i) different problems studied, (ii) computational challenges for solving those problems, and (iii) proposed methodologies. Finally, we list out important future research directions.

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Section: Han Et Al [45]mentioning

confidence: 99%

A Survey on Mining and Analysis of Uncertain Graphs

Banerjee¹

2021

Preprint

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“…Yuan et al, [129] realized that, in real applications, the graph data are often noisy, incomplete and inaccurate. In other words, there are many uncertain graphs.…”

Section: )mentioning

confidence: 99%

“…In other words, there are many uncertain graphs. Therefore, in [129], Yuan et al, studied pattern matching in a large uncertain graph. Specif-ically, they aimed to retrieve all qualified matches of a query pattern in the uncertain graph.…”

Section: )mentioning

confidence: 99%

Incremental Graph Pattern Based Node Matching

Sun

Liu

Wang

et al. 2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

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for their insightful and patient supervision during my PhD journey. Over the past few years, their professional suggestions kept me researching in the correct direction. Working with them let me truly understand the dedication spirit and the rigorous work attitude. It is my great honour to have them as my supervisors at Macquarie University. My academic journey would not have been so rewarding without their kindness and wisdom.

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“…Pattern matching queries over uncertain networks identify all occurrences of a query graph in an uncertain graph with probability of existence more than a predefined threshold. We shall present state-of-the-art indexing and pruning techniques [12,11,21,19] for pattern matching queries over uncertain graphs.…”

Section: Pattern Matching Queriesmentioning

confidence: 99%

On uncertain graphs modeling and queries

Khan

Chen

2015

Proc. VLDB Endow.

Self Cite

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Large-scale, highly-interconnected networks 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 research community. While many classical graph algorithms such as reachability and shortest path queries become #P-complete, and hence, more expensive in uncertain graphs; various complex queries are also emerging over uncertain networks, such as pattern matching, information diffusion, and influence maximization queries. In this tutorial, 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. We emphasize the current challenges and highlight some future research directions.

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Pattern Match Query in a Large Uncertain Graph

Cited by 9 publications

References 38 publications

A Survey on Mining and Analysis of Uncertain Graphs

A Survey on Mining and Analysis of Uncertain Graphs

Incremental Graph Pattern Based Node Matching

On uncertain graphs modeling and queries

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