Titlebook: On Uncertain Graphs; Arijit Khan,Yuan Ye,Lei Chen Book 2018 Springer Nature Switzerland AG 2018

scotoma

Reliability Queries,able. Reliability has been well-studied in the context of device networks (e.g., telecommunication networks), i.e., networks whose nodes are electronic devices and the (physical) links between such devices have a probability of failure [Aggarwal et al., 1975]. More recently, the attention has been s

Enteropathic

Graph Pattern Matching Queries,ely due to the many applications that require such efficient solutions, including protein complex prediction, social network analysis, and structural pattern recognition. However, in many real applications, the graph data are often noisy, incomplete, and inaccurate. In other words, there exist many

mettlesome

Graph Similarity Search Queries,ication in many fields, including bioinfor-matics, social network analysis, and Resource Description Framework (RDF) data management. However, in reality, graphs are often noisy and uncertain due to various factors, such as errors in data extraction, inconsistencies in data integration, and for priv

Oligarchy

Influence Maximization,Much of its benefit is embedded in the information flows in social networks. These information flows arise as a result of the communication between different entities in the network. Social networks, in fact, have advantages as a medium for fast, widespread information cascade as in the case of the

祖传

Major Open Problems,With the availability of uncertain networks and their applications in real world, the last ten years has seen an unprecedented interest and research in designing algorithms for querying and mining of uncertain graphs. There are yet many questions that need to be investigated. We conclude this book by highlighting some future research directions.

MENT

Modify

978-3-031-00732-3Springer Nature Switzerland AG 2018

ADORE

On Uncertain Graphs978-3-031-01860-2Series ISSN 2153-5418 Series E-ISSN 2153-5426

被诅咒的人

Meditative

Graph Similarity Search Queries,rity search over uncertain graphs is NP-hard, we employ a . framework to speed up these two queries. For the subgraph similarity query, in the . phase, we develop tight lower and upper bounds of . based on a . (PMI). During the . phase, we develop an efficient sampling algorithm to validate the rema