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Graph Mining

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Encyclopedia of Machine Learning and Data Mining

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Definition

Graph Mining is the set of tools and techniques used to (a) analyze the properties of real-world graphs, (b) predict how the structure and properties of a given graph might affect some application, and (c) develop models that can generate realistic graphs that match the patterns found in real-world graphs of interest.

Motivation and Background

A graph G = (V, E) consists of a set of edges, E connec-ting pairs of nodes from the set V ; extensions allow for weights and labels on both nodes and edges. Graphs edges can be used to point from one node to another, in which case the graph is called directed; in an undirected graph, edges must point both ways: i → j ⇔ j → i. A variant is the bipartite graph G = (V1, V2, E) where only edges linking nodes in V1 to nodes in V2 are allowed.

A graph provides a representation of the binary relationships between individual entities, and thus is an extremely common data structure. Examples include the graph of hyperlinks linking HTML...

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Recommended Reading

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Author information

Authors and Affiliations

  1. Yahoo! Research, Sunnyvale, CA, USA

    Deepayan Chakrabarti

Authors
  1. Deepayan Chakrabarti
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Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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© 2017 Springer Science+Business Media New York

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Chakrabarti, D. (2017). Graph Mining. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_350

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