Mining Graph Data Preface. Acknowledgments. Contributors. Chapter 1. Introduction (Lawrence Holder and Diane Cook). 1.1 Terminology. 1.2 Graph Databases. 1.3 Book Overview. References. PART I GRAPHS. Chapter 2. Graph Matching - Exact and Error-Tolerant Methods and the Automatic Learning of Edit Costs (Horst Bunke and Michel Neuhaus). 2.1 Introduction. 2.2 Definitions and Graph Matching Methods. 2.3 Learning Edit Costs. 2.4 Experimental Evaluation. 2.5 Discussion and Conclusions. References. Chapter 3. Graph Visualization and Data Mining (Walter Didimo and Giuseppe Liotta). 3.1 Introduction. 3.2 Graph Drawing Techniques. 3.3 Examples of Visualization Systems. 3.4 Conclusions. References. Chapter 4. Graph Patterns and the R-MAT Generator (Deepayan Chakrabarti and Christos Faloutsos). 4.1 Introduction. 4.2 Background and Related Work. 4.3 NetMine and R-MAT. 4.4 Experiments. 4.5 Conclusions. References. PART II MINING TECHNIQUES. Chapter 5. Discovery of Frequent Substructures (Xifeng Yan and Jiawei Han). 5.1 Introduction. 5.2 Preliminary Concepts. 5.3 Apriori-based Approach. 5.4 Pattern Growth Approach. 5.5 Variant Substructure Patterns. 5.6 Experiments and Performance Study. 5.7 Conclusions. References. Chapter 6. Finding Topological Frequent Patterns from Graph Datasets (Michihiro Kuramochi and George Karypis). 6.1 Introduction. 6.2 Background Definitions and Notation. 6.3 Frequent Pattern Discovery from Graph Datasets-Problem Definitions. 6.4 FSG for the Graph-Transaction Setting. 6.5 SIGRAM for the Single-Graph Setting. 6.6 GREW-Scalable Frequent Subgraph Discovery Algorithm. 6.7 Related Research. 6.8 Conclusions. References. Chapter 7. Unsupervised and Supervised Pattern Learning in Graph Data (Diane Cook, Lawrence Holder and Nikhil Ketkar). 7.1 Introduction. 7.2 Mining Graph Data Using Subdue. 7.3 Comparison to Other Graph-Based Mining Algorithms. 7.4 Comparison to Frequent Substructure Mining Approaches. 7.5 Comparison to ILP Approaches. 7.6 Conclusions. References. Chapter 8. Graph Grammar Learning (Istvan Jonyer). 8.1 Introduction. 8.2 Related Work. 8.3 Graph Grammar Learning. 8.4 Empirical Evaluation. 8.5 Conclusion. References. Chapter 9. Constructing Decision Trees Based On Chunkingless Graph-Based Induction (Kouzou Ohara, Phu Chien Nguyen, Akira Mogi, Hiroshi Motoda and Takashi Washio). 9.1 Introduction. 9.2 Graph-Based Induction Revisited. 9.3 Problem Caused by Chunking in B-GBI. 9.4 Chunkingless Graph-Based Induction (Cl-GBI). 9.5 Decision Tree Chunkingless Graph-Based Induction (DT-ClGBI). 9.6 Conclusions. References. Chapter 10. Some Links Between Formal Concept Analysis and Graph Mining (Michel Liquiere). 10.1 Presentation. 10.2 Basic Concepts and Notation. 10.3 Formal Concept Analysis. 10.4 Extension Lattice and Description Lattice Give Concept Lattice. 10.5 Graph Description and