Graph Mining

Abstract

AbstractThe contents of the book have focused so far on the mining of data where the underlying structure is characterized by special types of graphs where cycles are not allowed, i.e. acyclic graphs or trees. The focus of this chapter is on the frequent pattern mining problem where the underlying structure of the data can be of general graph type where cycles are allowed. These kinds of representations allow one to model complex aspects of the domain such as chemical compounds, networks, the Web, bioinformatics, etc. Generally speaking, graphs have many undesirable theoretical properties with respect to algorithmic complexity. In the graph mining problem, the common requirement is the systematic enumeration of sub-graphs from a given graph, known as the frequent subgraph mining problem. From the available graph analysis methods, we will narrow our focus to this problem as it is the prerequisite for the detection of interesting associations among graph-structured data objects, and has many important applications. For an extensive overview of graph mining in a general context, including different laws, data generators and algorithms, please refer to (Chakrabati & Faloutsos 2006; Washio & Motoda 2003, Han & Kamber 2006). Due to the existence of cycles in a graph, the frequent subgraph mining problem is much more complex than the frequent subtree mining problem. Even though theoretically it is an NP complete problem, in practice, a number of approaches are very applicable to the analysis of real-world graph data. We will look at a number of different approaches to the frequent subgraph mining problem and a number of approaches for the analysis of graph data in general.KeywordsMinimum Description LengthInductive Logic ProgrammingSubgraph IsomorphismGraph MiningFrequent SubgraphThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.