# Graph Mining

**DOI:**https://doi.org/10.1007/978-0-387-30164-8_350

## 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* = (*V*_{1}, *V*_{2}, *E*) where only edges linking nodes in *V*_{1} to nodes in *V*_{2} 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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