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Graph Mining: Laws and Generators

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Book cover Managing and Mining Graph Data

Part of the book series: Advances in Database Systems ((ADBS,volume 40))

Abstract

How does the Web look? How could we tell an “abnormal” social network from a “normal” one? These and similar questions are important in many fields where the data can intuitively be cast as a graph; examples range from computer networks, to sociology, to biology, and many more. Indeed, any M : N relation in database terminology can be represented as a graph. Many of these ques- tions boil down to the following: “How can we generate synthetic but realistic graphs?” To answer this, we must first understand what patterns are common in real-world graphs, and can thus be considered a mark of normality/realism. This survey gives an overview of the incredible variety of work that has been done on these problems. One of our main contributions is the integration of points of view from physics, mathematics, sociology and computer science.

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Authors and Affiliations

  1. Yahoo! Research, Sunnyvale, CA, USA

    Deepayan Chakrabarti

  2. School of Computer Science Carnegie Mellon University, Pittsburgh, PA, USA

    Christos Faloutsos & Mary McGlohon

Authors
  1. Deepayan Chakrabarti
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  2. Christos Faloutsos
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  3. Mary McGlohon
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Correspondence to Deepayan Chakrabarti .

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Editors and Affiliations

  1. Thomas J. Watson Research Center, IBM, Skyline Drive 19, Hawthorne, 10532, U.S.A.

    Charu C. Aggarwal

  2. Microsoft Research Asia, Zhichun Road 49, Beijing, 100080, China, People's Republic

    Haixun Wang

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Chakrabarti, D., Faloutsos, C., McGlohon, M. (2010). Graph Mining: Laws and Generators. In: Aggarwal, C., Wang, H. (eds) Managing and Mining Graph Data. Advances in Database Systems, vol 40. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6045-0_3

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