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Link and Graph Mining in the Big Data Era

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Handbook of Big Data Technologies

Abstract

Graphs are a convenient representation for large sets of data, being complex networks, social networks, publication networks, and so on. The growing volume of data modeled as complex networks, e.g. the World Wide Web, and social networks like Twitter, Facebook, has raised a new area of research focused in complex networks mining. In this new multidisciplinary area, it is possible to highlight some important tasks: extraction of statistical properties, community detection, link prediction, among several others. This new approach has been driven largely by the growing availability of computers and communication networks, which allow us to gather and analyze data on a scale far larger than previously possible. In this chapter we will give an overview of several graph mining approach to mine and handle large complex networks.

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Notes

  1. 1.

    http://dblp.uni-trier.de.

  2. 2.

    https://perso.uclouvain.be/vincent.blondel/research/louvain.html.

  3. 3.

    https://www.google.com/insidesearch/howsearchworks/algorithms.html.

  4. 4.

    http://newsroom.fb.com/news/2013/01/introducing-graph-search-beta/.

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Authors and Affiliations

  1. IBM Research, São Paulo, Brazil

    Ana Paula Appel

  2. CONICET and Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Cuyo, Mendoza, Argentina

    Luis G. Moyano

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  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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Appel, A.P., Moyano, L.G. (2017). Link and Graph Mining in the Big Data Era. In: Zomaya, A., Sakr, S. (eds) Handbook of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-49340-4_17

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