Mining Graph Data,9780471731900

Mining Graph Data

by ;
Edition: 1st
ISBN13: 9780471731900
ISBN10: 0471731900
Format: Hardcover
Pub. Date: 2006-11-28
Publisher(s): Wiley-Interscience
List Price: $186.61

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Summary

This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal background in analyzing graph data, with this book you'll be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets. 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.

Author Biography

DIANE J. COOK, PhD, is the Huie-Rogers Chair Professor in the School of Electrical Engineering and Computer Science at Washington State University. Her extensive research in artificial intelligence and data mining has been supported by grants from the National Science Foundation, NASA, DARPA, and Texas Instruments. Dr. Cook is the coauthor of Smart Environments: Technology, Protocols, and Applications (Wiley).

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.

Table of Contents

Preface xiii
Acknowledgments xv
Contributors xvii
Introduction
1(14)
Lawrence B. Holder
Diane J. Cook
Terminology
2(1)
Graph Databases
3(7)
Book Overview
10(5)
References
11(4)
Part I GRAPHS
15(82)
Graph Matching---Exact and Error-Tolerant Methods and the Automatic Learning of Edit Costs
17(18)
Horst Bunke
Michel Neuhaus
Introduction
17(1)
Definitions and Graph Matching Methods
18(6)
Learning Edit Costs
24(4)
Experimental Evaluation
28(3)
Discussion and Conclusions
31(4)
References
32(3)
Graph Visualization and Data Mining
35(30)
Walter Didimo
Giuseppe Liotta
Introduction
35(3)
Graph Drawing Techniques
38(10)
Examples of Visualization Systems
48(7)
Conclusions
55(10)
References
57(8)
Graph Patterns and the R-Mat Generator
65(32)
Deepayan Chakrabarti
Christos Faloutsos
Introduction
65(2)
Background and Related Work
67(12)
NetMine and R-MAT
79(3)
Experiments
82(4)
Conclusions
86(11)
References
92(5)
Part II MINING TECHNIQUES
97(248)
Discovery of Frequent Substructures
99(18)
Xifeng Yan
Jiawei Han
Introduction
99(1)
Preliminary Concepts
100(1)
Apriori-based Approach
101(2)
Pattern Growth Approach
103(4)
Variant Substructure Patterns
107(2)
Experiments and Performance Study
109(3)
Conclusions
112(5)
References
113(4)
Finding Topological Frequent Patterns from Graph Datasets
117(42)
Michihiro Kuramochi
George Karypis
Introduction
117(1)
Background Definitions and Notation
118(4)
Frequent Pattern Discovery from Graph Datasets---Problem Definitions
122(5)
FSG for the Graph-Transaction Setting
127(4)
SiGraM for the Single-Graph Setting
131(10)
Grew---Scalable Frequent Subgraph Discovery Algorithm
141(8)
Related Research
149(2)
Conclusions
151(8)
References
154(5)
Unsupervised and Supervised Pattern Learning in Graph Data
159(24)
Diane J. Cook
Lawrence B. Holder
Nikhil Ketkar
Introduction
159(1)
Mining Graph Data Using Subdue
160(5)
Comparison to Other Graph-Based Mining Algorithms
165(1)
Comparison to Frequent Substructure Mining Approaches
165(5)
Comparison to ILP Approaches
170(9)
Conclusions
179(4)
References
179(4)
Graph Grammar Learning
183(20)
Istvan Jonyer
Introduction
183(1)
Related Work
184(1)
Graph Grammar Learning
185(8)
Empirical Evaluation
193(6)
Conclusion
199(4)
References
199(4)
Constructing Decision Tree Based on Chunkingless Graph-Based Induction
203(24)
Kouzou Ohara
Phu Chien Nguyen
Akira Mogi
Hiroshi Motoda
Takashi Washio
Introduction
203(2)
Graph-Based Induction Revisited
205(2)
Problem Caused by Chunking in B-GBI
207(1)
Chunkingless Graph-Based Induction (CI-GBI)
208(6)
Decision Tree Chunkingless Graph-Based Induction (DT-ClGBI)
214(10)
Conclusions
224(3)
References
224(3)
Some Links Between Formal Concept Analysis and Graph Mining
227(26)
Michel Liquiere
Presentation
227(1)
Basic Concepts and Notation
228(1)
Formal Concept Analysis
229(2)
Extension Lattice and Description Lattice Give Concept Lattice
231(4)
Graph Description and Galois Lattice
235(5)
Graph Mining and Formal Propositionalization
240(9)
Conclusion
249(4)
References
250(3)
Kernel Methods for Graphs
253(30)
Thomas Gartner
Tamas Horvath
Quoc V. Le
Alex J. Smola
Stefan Wrobel
Introduction
253(1)
Graph Classification
254(12)
Vertex Classification
266(13)
Conclusions and Future Work
279(4)
References
280(3)
Kernels as Link Analysis Measures
283(28)
Masashi Shimbo
Takahiko Ito
Introduction
283(1)
Preliminaries
284(2)
Kernel-based Unified Framework for Importance and Relatedness
286(4)
Laplacian Kernels as a Relatedness Measure
290(7)
Practical Issues
297(2)
Related Work
299(1)
Evaluation with Bibliographic Citation Data
300(8)
Summary
308(3)
References
308(3)
Entity Resolution in Graphs
311(34)
Indrajit Bhattacharya
Lise Getoor
Introduction
311(3)
Related Work
314(4)
Motivating Example for Graph-Based Entity Resolution
318(4)
Graph-Based Entity Resolution: Problem Formulation
322(3)
Similarity Measures for Entity Resolution
325(5)
Graph-Based Clustering for Entity Resolution
330(3)
Experimental Evaluation
333(8)
Conclusion
341(4)
References
342(3)
Part III APPLICATIONS
345(124)
Mining from Chemical Graphs
347(34)
Takashi Okada
Introduction and Representation of Molecules
347(8)
Issues for Mining
355(1)
CASE: A Prototype Mining System in Chemistry
356(2)
Quantitative Estimation Using Graph Mining
358(4)
Extension of Linear Fragments to Graphs
362(4)
Combination of Conditions
366(9)
Concluding Remarks
375(6)
References
377(4)
Unified Approach to Rooted Tree Mining: Algorithms and Applications
381(30)
Mohammed Zaki
Introduction
381(1)
Preliminaries
382(2)
Related Work
384(1)
Generating Candidate Subtrees
385(7)
Frequency Computation
392(5)
Counting Distinct Occurrences
397(2)
The SLEUTH Algorithm
399(2)
Experimental Results
401(4)
Tree Mining Applications in Bioinformatics
405(4)
Conclusions
409(2)
References
409(2)
Dense Subgraph Extraction
411(32)
Andrew Tomkins
Ravi Kumar
Introduction
411(3)
Related Work
414(2)
Finding the densest subgraph
416(2)
Trawling
418(3)
Graph Shingling
421(8)
Connection Subgraphs
429(9)
Conclusions
438(5)
References
438(5)
Social Network Analysis
443(26)
Sherry E. Marcus
Melanie Moy
Thayne Coffman
Introduction
443(1)
Social Network Analysis
443(9)
Group Detection
452(1)
Terrorist Modus Operandi Detection System
452(13)
Computational Experiments
465(2)
Conclusion
467(2)
References
468(1)
Index 469

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