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Big-Graphs: Querying, Mining, and Beyond

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Handbook of Big Data Technologies

Abstract

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.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Arijit Khan

  2. Department of Computer Science and Engineering, IIT Madras, Chennai, India

    Sayan Ranu

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  1. Arijit Khan
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  2. Sayan Ranu
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Correspondence to Arijit Khan .

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Editors and Affiliations

  1. School of Information Technologies, The University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

  2. The School of Computer Science, The University of New South Wales, Eveleigh, New South Wales, Australia

    Sherif Sakr

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Khan, A., Ranu, S. (2017). Big-Graphs: Querying, Mining, and Beyond. In: Zomaya, A., Sakr, S. (eds) Handbook of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-49340-4_16

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