The amount of available data is increasing very fast. With this data, the desire for data mining is also growing. More and larger databases have to be searched to find interesting (and frequent) elements and connections between them. Most often the data of interest is very complex. It is common to model complex data with the help of graphs consisting of nodes and edges that are often labeled to store additional information. Having a graph database, the main goal is to find connections and similarities between its graphs. Based on these connections and similarities, the graphs can be categorized, clustered or changed according to the application area. Regularly occurring patterns in the form of subgraphs —called fragments in this context—that appear at least in a certain percentage of graphs, are a common method to analyze graph databases. The actual occurrence of a fragment in a database graph is called embedding. Finding the fragments and their embeddings is the goal of subgraph mining described in detail in this chapter. The first published graph mining algorithm, called Subdue, appeared in the mid-1990s and is still used in different application areas and was extended in several ways. (Cook & Holder, 2000). Subdue is based on a heuristic search and does not find all possible fragments and embeddings. It took a few more years before more and faster approaches appeared. In (Helma, Kramer, & de Raedt, 2002) graph databases are mined for simple paths, for a lot of other applications only trees are of interest (Rückert & Kramer, 2004). Also Inductive Logic Programming (Finn et al., 1998) was applied in this area. At the beginning of the new millennium finally more and more and every time faster approaches for general mining of graph databases were developed that were able to find all possible fragments. (Borgelt & Berthold, 2002; Yan & Han, 2002; Kuramochi & Karypis, 2001; Nijssen & Kok, 2004). Several different application areas for graph mining are researched. The most common area is mining molecular databases where the molecules are displayed by their two-dimensional structure. When analyzing molecules it is interesting to find patterns that might explain why a certain set of molecules is useful as a drug against certain diseases (Borgelt & Berthold, 2002). Similar problems occur for protein databases. Here graph data mining can be used to find structural patterns in the primary, secondary and tertiary structure of protein categories (Cook & Holder, 2000). Another application area are web searches (Cook, Manocha, & Holder, 2003). Existing search engines use linear feature matches. Using graphs as underlying data structure, nodes represent pages, documents or document keywords and edges represent links between them. Posing a query as a graph means a smaller graph has to be embedded in the larger one. The graph modeling the data structure can be mined to find similar clusters. Quite new is the application of subgraph mining in optimizing code for embedded devices. With the help of so-called procedural abstraction, the size of pre-compiled binaries can be reduced which is often crucial because of the limited storage capacities of embedded systems. There, subgraph mining helps identifying common structures in the program’s control flow graph which can then be combined (“abstracted”) into a single procedure (Dreweke et.al., 2007).
Concept and assumptions about the temporal graph database