In many fields, the data can be represented as a graph. For example, the relationships between people in a social network can be viewed as a graph (vertices are people and edges are relationships). Some other examples are chemical molecules and roads between cities, which can be also viewed as graphs. To analyze graph data, several algorithms have been developped. In subgraph mining, the goal is to find subgraphs that are interesting in one or many graphs. The meaning of interesting depends on the application. In the basic problem of frequent subgraph mining, the goal is to find subgraphs that are frequent (appear many times in one or many graphs). Finding these frequent subgraphs can help to understand the structure of graphs and also be used for other applications such as classification. This page gives an overview of subgraph mining.

There are also many types of graphs that can be analyzed (e.g. directed graphs, attributed graphs, trees, etc.).

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