Mining Graph Data图形数据采掘
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作者： 本社 编
出 版 社： 吉林长白山
出版时间： 2006-11-1字数：版次： 1页数： 479印刷时间： 2006/11/01开本： 16开印次： 1纸张： 胶版纸I S B N ： 9780471731900包装： 精装内容简介
“individuals with no background analyzing graph data can learn how to represent the data as graphs，extract patterns or concepts from the data，and see how researchers apply the methodologies to real datasets。”（Computing Reviews。com， March 23， 2007）
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。
目录
Preface
Acknowledgments
Contributors
1 INTRODUCTION
Lawrence B.Holder and Diane J.Cook
1.1 Terminology
1.2 Graph Databases
1.3 Book 0verview
Rererences
PartⅠ GRAPHS
2 GRAPH MATcHING—ExAcT AND ERROR.TOLERANT METHODS AND THE AUToMATIC LEARNING OF EDIT COSTS
Horst Bunke and Michef 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
Refefences
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
RefeFences
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
PartⅡ MINING TECHNIQUES
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 PerfoFinance Study
5.7 Conclusions
RefeFences
6 FlNDlNG TOPOLOGICAL FREQUENT PATTERNS FROM
GRAPH DATASETS
Michihiro Kuramochi and George Karypis
6.1 IntrOduction
6.2 Background Definitions and Noration
6.3 Frequent Pattern Discovery from Graph Datasets—Problem Definitions
6.4 FSG for the Graph—Transaction Setting
6.5 S JGRAM for the Single—Graph Setting
6.6 GREW——Scalable Frequent Subgraph Discovery Algorithm
6.7 Related Research
6.8 Conclusions
RefeFences
7 UNSUPERVISED AND SUPERVISED PATTERN LEARNING
lN GRAPH DATA
Diane J.Cook。Lawrence B.Holder,and Nikhil Ketkar
7.1 IntrOduction
……
8 Graph Grammar Learning
9 CONSTRUCTING DECISION TREE BASED ON CHUNKINGLESS GRAPH-BASED INDUCTION
10 Some Links between Formal Concept Analysis and Graph Mining
11 Kernel Methods for Graphs
12 Kernels as Link Analysis Measures
13 Entity Resolution in Graphs
Part Ⅲ：Applications
14 Mining from Chemical Graphs
15 Unified Approach to Rooted Tree Mining: Algorithms and Applications
16 Dense Subgraph Extraction
17 Social Network Analysis
Index
