Contents

Cover

Title Page

Copyright

Dedication

Preface

Contributors

Chapter 1: A Survey of Computational Approaches to Reconstruct and Partition Biological Networks

1.1 Introduction

1.2 Biological Networks

1.3 Genome-wide Measurements

1.4 Reconstruction of Biological Networks

1.5 Partitioning Biological Networks

1.6 Discussion

References

Chapter 2: Introduction to Complex Networks: Measures, Statistical Properties, and Models

2.1 Introduction

2.2 Representation of Networks

2.3 Classical Network

2.4 Scale-Free Network

2.5 Small-World Network

2.6 Clustered Network

2.7 Hierarchical Modularity

2.8 Network Motif

2.9 Assortativity

2.10 Reciprocity

2.11 Weighted Networks

2.12 Network Complexity

2.13 Centrality

2.14 Conclusion

References

Chapter 3: Modeling for Evolving Biological Networks

3.1 Introduction

3.2 Unified Evolving Network Model: Reproduction of Heterogeneous Connectivity, Hierarchical Modularity, and Disassortativity

3.3 Modeling Without Parameter Tuning: A Case Study of Metabolic Networks

3.4 Bipartite Relationship: A Case Study of Metabolite Distribution

3.5 Conclusion

References

Chapter 4: Modularity Configurations in Biological Networks with Embedded Dynamics

4.1 Introduction

4.2 Methods

4.3 Results

4.4 Discussion and Concluding Remarks

Acknowledgment

Supporting Information

References

Chapter 5: Influence of Statistical Estimators on the Large-Scale Causal Inference of Regulatory Networks

5.1 Introduction

5.2 Methods

5.3 Results

5.4 Conclusion and Summary

Acknowledgment

References

Chapter 6: Weighted Spectral Distribution: A Metric for Structural Analysis of Networks

6.1 Introduction

6.2 Weighted Spectral Distribution

6.3 A Simple Worked Example

6.4 The Internet Autonomous System Topology

6.5 Comparing Topology Generators

6.6 Tuning Topology Generator Parameters

6.7 Generating Topologies with Optimum Parameters

6.8 Internet Topology Evolution

6.9 Conclusions

References

Chapter 7: The Structure of an Evolving Random Bipartite Graph

7.1 Introduction

7.2 The Structure of a Sparse Bipartite Graph

7.3 Enumerating Bipartite Graphs

7.4 Asymptotic Expansion via the Saddle Point Method

7.5 Proofs of the Main Theorems

7.6 Empirical Data

7.7 Conclusion and Summary

References

Chapter 8: Graph Kernels

8.1 Introduction

8.2 Convolution Kernels

8.3 Random Walk Graph Kernels

8.4 Path-Based Graph Kernels

8.5 Tree-Pattern Graph Kernels

8.6 Cyclic Pattern Kernels

8.7 Graphlet Kernels

8.8 Optimal Assignment Kernels

8.9 Other Graph Kernels

8.10 Applications in Bio-and Cheminformatics

8.11 Summary and Conclusions

Acknowledgments

References

Chapter 9: Network-Based Information Synergy Analysis for Alzheimer Disease

9.1 Introduction

9.2 Datasets and Methods

9.3 Results

9.4 Summary and Conclusions

Acknowledgment

References

Chapter 10: Density-Based Set Enumeration in Structured Data

10.1 Introduction

10.2 Unsupervised Pattern Discovery in Structured Data

10.3 Dense Cluster Enumeration in Weighted Interaction Networks

10.4 Dense Cluster Enumeration in Higher-Order Association Data

10.5 Discussion

References

Chapter 11: Hyponym Extraction Employing a Weighted Graph Kernel

11.1 Introduction

11.2 Related Work

11.3 Drawbacks of Current Approaches

11.4 Semantic Networks Following the MultiNet Formalism

11.5 Support Vector Machines and Kernels

11.6 Architecture

11.7 Graph Kernel

11.8 Graph Kernel Extensions

11.9 Distance Weighting

11.10 Features for Hyponymy Extraction

11.11 Evaluation

11.12 Conclusion and Outlook

Acknowledgments

References

Index