Mining Graph DataISBN: 9780471731900
500 pages
November 2006

There is a misprint with the link to the accompanying Web page for this book. For those readers who would like to experiment with the techniques found in this book or test their own ideas on graph data, the Web page for the book should be http://www.eecs.wsu.edu/MGD.
Acknowledgments.
Contributors.
1 INTRODUCTION (Lawrence B. Holder and Diane J. Cook).
1.1 Terminology.
1.2 Graph Databases.
1.3 Book Overview.
References.
Part I GRAPHS.
2 GRAPH MATCHING—EXACT AND ERRORTOLERANT 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.
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.
4 GRAPH PATTERNS AND THE RMAT GENERATOR (Deepayan Chakrabarti and Christos Faloutsos).
4.1 Introduction.
4.2 Background and Related Work.
4.3 NetMine and RMAT.
4.4 Experiments.
4.5 Conclusions.
References.
Part II MINING TECHNIQUES.
5 DISCOVERY OF FREQUENT SUBSTRUCTURES (Xifeng Yan and Jiawei Han).
5.1 Introduction.
5.2 Preliminary Concepts.
5.3 Aprioribased Approach.
5.4 Pattern Growth Approach.
5.5 Variant Substructure Patterns.
5.6 Experiments and Performance Study.
5.7 Conclusions.
References.
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 GraphTransaction Setting.
6.5 SIGRAM for the SingleGraph Setting.
6.6 GREW—Scalable Frequent Subgraph Discovery Algorithm.
6.7 Related Research.
6.8 Conclusions.
References.
7 UNSUPERVISED AND SUPERVISED PATTERN LEARNING IN GRAPH DATA (Diane J. Cook, Lawrence B. Holder, and Nikhil Ketkar).
7.1 Introduction.
7.2 Mining Graph Data Using Subdue.
7.3 Comparison to Other GraphBased Mining Algorithms.
7.4 Comparison to Frequent Substructure Mining Approaches.
7.5 Comparison to ILP Approaches.
7.6 Conclusions.
References.
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.
9 CONSTRUCTING DECISION TREE BASED ON CHUNKINGLESS GRAPHBASED INDUCTION (Kouzou Ohara, Phu Chien Nguyen, Akira Mogi, Hiroshi Motoda, and Takashi Washio).
9.1 Introduction.
9.2 GraphBased Induction Revisited.
9.3 Problem Caused by Chunking in BGBI.
9.4 Chunkingless GraphBased Induction (ClGBI).
9.5 Decision Tree Chunkingless GraphBased Induction (DTClGBI).
9.6 Conclusions.
References.
10 SOME LINKS BETWEEN FORMAL CONCEPT ANALYSIS AND GRAPH MINING (Michel Liquière).
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 Galois Lattice.
10.6 Graph Mining and Formal Propositionalization.
10.7 Conclusion.
References.
11 KERNEL METHODS FOR GRAPHS (Thomas Gärtner, Tamás Horváth, Quoc V. Le, Alex J. Smola, and Stefan Wrobel).
11.1 Introduction.
11.2 Graph Classification.
11.3 Vertex Classification.
11.4 Conclusions and Future Work.
References.
12 KERNELS AS LINK ANALYSIS MEASURES (Masashi Shimbo and Takahiko Ito).
12.1 Introduction.
12.2 Preliminaries.
12.3 Kernelbased Unified Framework for Importance and Relatedness.
12.4 Laplacian Kernels as a Relatedness Measure.
12.5 Practical Issues.
12.6 Related Work.
12.7 Evaluation with Bibliographic Citation Data.
12.8 Summary.
References.
13 ENTITY RESOLUTION IN GRAPHS (Indrajit Bhattacharya and Lise Getoor).
13.1 Introduction.
13.2 Related Work.
13.3 Motivating Example for GraphBased Entity Resolution.
13.4 GraphBased Entity Resolution: Problem Formulation.
13.5 Similarity Measures for Entity Resolution.
13.6 GraphBased Clustering for Entity Resolution.
13.7 Experimental Evaluation.
13.8 Conclusion.
References.
Part III APPLICATIONS.
14 MINING FROM CHEMICAL GRAPHS (Takashi Okada).
14.1 Introduction and Representation of Molecules.
14.2 Issues for Mining.
14.3 CASE: A Prototype Mining System in Chemistry.
14.4 Quantitative Estimation Using Graph Mining.
14.5 Extension of Linear Fragments to Graphs.
14.6 Combination of Conditions.
14.7 Concluding Remarks.
References.
15 UNIFIED APPROACH TO ROOTED TREE MINING: ALGORITHMS AND APPLICATIONS (Mohammed Zaki).
15.1 Introduction.
15.2 Preliminaries.
15.3 Related Work.
15.4 Generating Candidate Subtrees.
15.5 Frequency Computation.
15.6 Counting Distinct Occurrences.
15.7 The SLEUTH Algorithm.
15.8 Experimental Results.
15.9 Tree Mining Applications in Bioinformatics.
15.10 Conclusions.
References.
16 DENSE SUBGRAPH EXTRACTION (Andrew Tomkins and Ravi Kumar).
16.1 Introduction.
16.2 Related Work.
16.3 Finding the densest subgraph.
16.4 Trawling.
16.5 Graph Shingling.
16.6 Connection Subgraphs.
16.7 Conclusions.
References.
17 SOCIAL NETWORK ANALYSIS (Sherry E. Marcus, Melanie Moy, and Thayne Coffman).
17.1 Introduction.
17.2 Social Network Analysis.
17.3 Group Detection.
17.4 Terrorist Modus Operandi Detection System.
17.5 Computational Experiments.
17.6 Conclusion.
References.
Index.
LAWRENCE B. HOLDER, PhD, is Professor in the School of Electrical Engineering and Computer Science at Washington State University, where he teaches and conducts research in artificial intelligence, machine learning, data mining, graph theory, parallel and distributed processing, and cognitive architectures.