Managing and Mining Graph Data

Graph mining and management has become an important topic of research re- cently because of numerous applications to a wide variety of data mining prob- lems in computational biology, chemical data analysis, drug discovery and com- munication networking. Traditional data mining and management algorithms such as clustering, classification, frequent pattern mining and indexing have now been extended to the graph scenario. This book contains a number of chapters which are carefully chosen in order to discuss the broad research issues in graph management and mining. In addition, a number of important applications of graph mining are also covered in the book. The purpose of this chapter is to provide an overview of the different kinds of graph processing and mining tech- niques, and the coverage of these topics in this book.