Big-Graphs: Querying, Mining, and Beyond

  • Arijit KhanEmail author
  • Sayan Ranu


Graphs are a ubiquitous model to represent objects and their relations. However, the complex combinations of structure and content, coupled with massive volume, high streaming rate, and uncertainty inherent in the data, raise several challenges that require new efforts for smarter and faster graph analysis. With the advent of complex networks such as the World Wide Web, social networks, knowledge graphs, genome and scientific databases, Internet of things, medical and government records, novel graph computations are also emerging, including graph pattern matching and mining, similarity search, keyword search, and graph query-by-example. These workloads require both topology and content information of the network; and hence, they are different from classical graph computations such as shortest path, reachability, and minimum cut, which depend only on the structure of the network. In this chapter, we shall describe the emerging graph queries and mining problems, their applications and resolution techniques. We emphasize the current challenges and highlight some future research directions.


Resource Description Framework Constraint Satisfaction Problem Keyword Query Graph Database Query Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Department of Computer Science and EngineeringIIT MadrasChennaiIndia

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