Mining Large Graphs

Authored by: David F. Gleich , Michael W. Mahoney

Handbook of Big Data

Print publication date:  February  2016
Online publication date:  February  2016

Print ISBN: 9781482249071
eBook ISBN: 9781482249088
Adobe ISBN:

10.1201/b19567-17

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Abstract

Graphs provide a general representation or data model for many types of data, where pair-wise relationships are known or thought to be particularly important. * Thus, it should not be surprising that interest in graph mining has grown with the recent interest in big data. Much of the big data generated and analyzed involves pair-wise relationships among a set of entities. For example, in e-commerce applications such as with Amazon’s product database, customers are related to products through their purchasing activities; on the web, web pages are related through hypertext linking relationships; on social networks such as Facebook, individuals are related through their friendships; and so on. Similarly, in scientific applications, research articles are related through citations; proteins are related through metabolic pathways, co-expression, and regulatory network effects within a cell; materials are related through models of their crystalline structure; and so on.

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