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E-raamat: Structural Pattern Recognition with Graph Edit Distance: Approximation Algorithms and Applications

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Structural Pattern Recognition with Graph Edit Distance: Approximation Algorithms and ApplicationsSuurem pilt
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This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED). The book also provides a detailed review of a diverse selection of novel methods related to GED, and concludes by suggesting possible avenues for future research. Topics and features: formally introduces the concept of GED, and highlights the basic properties of this graph matching paradigm; describes a reformulation of GED to a quadratic assignment problem; illustrates how the quadratic assignment problem of GED can be reduced to a linear sum assignment problem; reviews strategies for reducing both the overestimation of the true edit distance and the matching time in the approximation framework; examines the improvement demonstrated by the described algorithmic framework with respect to the distance accuracy and the matching time; includes appendices listing the datasets employed for the experimental evaluations discussed in the book.

Arvustused

The book presents the use of graphs in the field of structural pattern recognition. The book is written in a very accessible fashion. The author gives many examples presenting the notations and problems considered. The book is suitable for graduate students and is an ideal reference for researchers and professionals interested in graph edit distance and its applications in pattern recognition. (Krzystof Gdawiec, zbMATH 1365.68004, 2017) 

This book is exactly about this fascinating topic: the definition, the study of properties, and the areas of application of the graph edit distance in the realm of structural pattern recognition. The books intended audience is advanced graduate students in science and engineering, but also professionals working in relevant fields. (Dimitrios Katsaros, Computing Reviews, computingreviews.com, August, 2016)

Part I Foundations and Applications of Graph Edit Distance
1 Introduction and Basic Concepts
3(26)
1.1 Pattern Recognition
3(2)
1.1.1 Statistical and Structural Pattern Recognition
4(1)
1.2 Graph and Subgraph
5(4)
1.3 Graph Matching
9(10)
1.3.1 Exact Graph Matching
10(5)
1.3.2 Error-Tolerant Graph Matching
15(4)
1.4 Outline of the Book
19(10)
References
20(9)
2 Graph Edit Distance
29(16)
2.1 Basic Definition and Properties
29(6)
2.1.1 Conditions on Edit Cost Functions
31(2)
2.1.2 Example Definitions of Cost Functions
33(2)
2.2 Computation of Exact Graph Edit Distance
35(3)
2.3 Graph Edit Distance-Based Pattern Recognition
38(7)
2.3.1 Nearest-Neighbor Classification
38(1)
2.3.2 Kernel-Based Classification
39(2)
2.3.3 Classification of Vector Space Embedded Graphs
41(1)
References
42(3)
3 Bipartite Graph Edit Distance
45(24)
3.1 Graph Edit Distance as Quadratic Assignment Problem
45(6)
3.2 Bipartite Graph Edit Distance
51(8)
3.2.1 Deriving Upper and Lower Bounds on the Graph Edit Distance
54(4)
3.2.2 Summary
58(1)
3.3 Experimental Evaluation
59(2)
3.4 Pattern Recognition Applications of Bipartite Graph Edit Distance
61(8)
References
62(7)
Part II Recent Developments and Research on Graph Edit Distance
4 Improving the Distance Accuracy of Bipartite Graph Edit Distance
69(32)
4.1 Change of Notation
69(1)
4.2 Improvements via Search Strategies
70(23)
4.2.1 Iterative Search
70(2)
4.2.2 Floating Search
72(2)
4.2.3 Genetic Search
74(2)
4.2.4 Greedy Search
76(2)
4.2.5 Genetic Search with Swap Strategy
78(2)
4.2.6 Beam Search
80(3)
4.2.7 Experimental Evaluation
83(10)
4.3 Improvements via Integration of Node Centrality Information
93(8)
4.3.1 Node Centrality Measures
93(2)
4.3.2 Integrating Node Centrality in the Assignment
95(1)
4.3.3 Experimental Evaluation
96(2)
References
98(3)
5 Learning Exact Graph Edit Distance
101(20)
5.1 Predicting Exact Graph Edit Distance from Lower and Upper Bounds
101(9)
5.1.1 Linear Support Vector Regression
101(2)
5.1.2 Nonlinear Support Vector Regression
103(1)
5.1.3 Predicting dλminfrom dψand d'ψ
104(1)
5.1.4 Experimental Evaluation
105(5)
5.2 Predicting the Correctness of Node Edit Operations
110(11)
5.2.1 Features for Node Edit Operations
110(3)
5.2.2 Experimental Evaluation
113(5)
References
118(3)
6 Speeding Up Bipartite Graph Edit Distance
121(14)
6.1 Suboptimal Assignment Algorithms
121(6)
6.1.1 Basic Greedy Assignment
122(1)
6.1.2 Tie Break Strategy
122(1)
6.1.3 Refined Greedy Assignment
123(1)
6.1.4 Greedy Assignment Regarding Loss
124(1)
6.1.5 Order of Node Processing
125(1)
6.1.6 Greedy Sort Assignment
126(1)
6.2 Relations to Exact Graph Edit Distance
127(1)
6.3 Experimental Evaluation
128(7)
References
134(1)
7 Conclusions and Future Work
135(4)
7.1 Main Contributions and Findings
135(1)
7.2 Future Work
136(3)
References
137(2)
8 Appendix A: Experimental Evaluation of Sorted Beam Search
139(10)
8.1 Sorting Criteria
139(3)
8.1.1 Confident
139(1)
8.1.2 Unique
140(1)
8.1.3 Divergent
140(1)
8.1.4 Leader
140(1)
8.1.5 Interval
141(1)
8.1.6 Deviation
141(1)
8.2 Experimental Evaluation
142(7)
References
148(1)
9 Appendix B: Data Sets
149(8)
9.1 LETTER Graphs
149(1)
9.2 GREC Graphs
150(1)
9.3 FP Graphs
151(1)
9.4 AIDS Graphs
152(1)
9.5 MUTA Graphs
153(1)
9.6 PROT Graphs
153(1)
9.7 GREYC Graphs
154(3)
References
155(2)
Index 157
Dr. Kaspar Riesen is a university lecturer of computer science in the Institute for Information Systems at the University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland.
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