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首页MANAGING AND MINING GRAPH DATA
Managing and Mining Graph Data is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing.
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MANAGING AND MINING GRAPH DATA
MANAGING AND MINING GRAPH DATA
Edited by
CHARU C. AGGARWAL
IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, USA
HAIXUN WANG
Microsoft Research Asia, Beijing, China 100190
Kluwer Academic Publishers
Boston/Dordrecht/London
Contents
List of Figures xv
List of Tables xxi
Preface xxiii
1
An Introduction to Graph Data
1
Charu C. Aggarwal and Haixun Wang
1. Introduction 1
2. Graph Management and Mining Applications 3
3. Summary 8
References 9
2
Graph Data Management and Mining: A Survey of Algorithms and Applications
13
Charu C. Aggarwal and Haixun Wang
1. Introduction 13
2. Graph Data Management Algorithms 16
2.1 Indexing and Query Processing Techniques 16
2.2 Reachability Queries 19
2.3 Graph Matching 21
2.4 Keyword Search 24
2.5 Synopsis Construction of Massive Graphs 27
3. Graph Mining Algorithms 29
3.1 Pattern Mining in Graphs 29
3.2 Clustering Algorithms for Graph Data 32
3.3 Classification Algorithms for Graph Data 37
3.4 The Dynamics of Time-Evolving Graphs 40
4. Graph Applications 43
4.1 Chemical and Biological Applications 43
4.2 Web Applications 45
4.3 Software Bug Localization 51
5. Conclusions and Future Research 55
References 55
3
Graph Mining: Laws and Generators
69
Deepayan Chakrabarti, Christos Faloutsos and Mary McGlohon
1. Introduction 70
2. Graph Patterns 71
vi MANAGING AND MINING GRAPH DATA
2.1 Power Laws and Heavy-Tailed Distributions 72
2.2 Small Diameters 77
2.3 Other Static Graph Patterns 79
2.4 Patterns in Evolving Graphs 82
2.5 The Structure of Specific Graphs 84
3. Graph Generators 86
3.1 Random Graph Models 88
3.2 Preferential Attachment and Variants 92
3.3 Optimization-based generators 101
3.4 Tensor-based 108
3.5 Generators for specific graphs 113
3.6 Graph Generators: A summary 115
4. Conclusions 115
References 117
4
Query Language and Access Methods for Graph Databases
125
Huahai He and Ambuj K. Singh
1. Introduction 126
1.1 Graphs-at-a-time Queries 126
1.2 Graph Specific Optimizations 127
1.3 GraphQL 128
2. Operations on Graph Structures 129
2.1 Concatenation 130
2.2 Disjunction 131
2.3 Repetition 131
3. Graph Query Language 132
3.1 Data Model 132
3.2 Graph Patterns 133
3.3 Graph Algebra 134
3.4 FLWR Expressions 137
3.5 Expressive Power 138
4. Implementation of the Selection Operator 140
4.1 Graph Pattern Matching 140
4.2 Local Pruning and Retrieval of Feasible Mates 142
4.3 Joint Reduction of Search Space 144
4.4 Optimization of Search Order 146
5. Experimental Study 148
5.1 Biological Network 148
5.2 Synthetic Graphs 150
6. Related Work 152
6.1 Graph Query Languages 152
6.2 Graph Indexing 155
7. Future Research Directions 155
8. Conclusion 156
Appendix: Query Syntax of GraphQL 156
References 157
5
Graph Indexing
161
Xifeng Yan and Jiawei Han
1. Introduction 161
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