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Statistical and Machine Learning Approaches for Network Analysis

Book Description

Explore the multidisciplinary nature of complex networks through machine learning techniques

Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks.

Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include:

  • A survey of computational approaches to reconstruct and partition biological networks

  • An introduction to complex networks—measures, statistical properties, and models

  • Modeling for evolving biological networks

  • The structure of an evolving random bipartite graph

  • Density-based enumeration in structured data

  • Hyponym extraction employing a weighted graph kernel

Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Dedication
  5. Preface
  6. Contributors
  7. Chapter 1: A Survey of Computational Approaches to Reconstruct and Partition Biological Networks
    1. 1.1 Introduction
    2. 1.2 Biological Networks
    3. 1.3 Genome-wide Measurements
    4. 1.4 Reconstruction of Biological Networks
    5. 1.5 Partitioning Biological Networks
    6. 1.6 Discussion
    7. References
  8. Chapter 2: Introduction to Complex Networks: Measures, Statistical Properties, and Models
    1. 2.1 Introduction
    2. 2.2 Representation of Networks
    3. 2.3 Classical Network
    4. 2.4 Scale-Free Network
    5. 2.5 Small-World Network
    6. 2.6 Clustered Network
    7. 2.7 Hierarchical Modularity
    8. 2.8 Network Motif
    9. 2.9 Assortativity
    10. 2.10 Reciprocity
    11. 2.11 Weighted Networks
    12. 2.12 Network Complexity
    13. 2.13 Centrality
    14. 2.14 Conclusion
    15. References
  9. Chapter 3: Modeling for Evolving Biological Networks
    1. 3.1 Introduction
    2. 3.2 Unified Evolving Network Model: Reproduction of Heterogeneous Connectivity, Hierarchical Modularity, and Disassortativity
    3. 3.3 Modeling Without Parameter Tuning: A Case Study of Metabolic Networks
    4. 3.4 Bipartite Relationship: A Case Study of Metabolite Distribution
    5. 3.5 Conclusion
    6. References
  10. Chapter 4: Modularity Configurations in Biological Networks with Embedded Dynamics
    1. 4.1 Introduction
    2. 4.2 Methods
    3. 4.3 Results
    4. 4.4 Discussion and Concluding Remarks
    5. Acknowledgment
    6. Supporting Information
    7. References
  11. Chapter 5: Influence of Statistical Estimators on the Large-Scale Causal Inference of Regulatory Networks
    1. 5.1 Introduction
    2. 5.2 Methods
    3. 5.3 Results
    4. 5.4 Conclusion and Summary
    5. Acknowledgment
    6. References
  12. Chapter 6: Weighted Spectral Distribution: A Metric for Structural Analysis of Networks
    1. 6.1 Introduction
    2. 6.2 Weighted Spectral Distribution
    3. 6.3 A Simple Worked Example
    4. 6.4 The Internet Autonomous System Topology
    5. 6.5 Comparing Topology Generators
    6. 6.6 Tuning Topology Generator Parameters
    7. 6.7 Generating Topologies with Optimum Parameters
    8. 6.8 Internet Topology Evolution
    9. 6.9 Conclusions
    10. References
  13. Chapter 7: The Structure of an Evolving Random Bipartite Graph
    1. 7.1 Introduction
    2. 7.2 The Structure of a Sparse Bipartite Graph
    3. 7.3 Enumerating Bipartite Graphs
    4. 7.4 Asymptotic Expansion via the Saddle Point Method
    5. 7.5 Proofs of the Main Theorems
    6. 7.6 Empirical Data
    7. 7.7 Conclusion and Summary
    8. References
  14. Chapter 8: Graph Kernels
    1. 8.1 Introduction
    2. 8.2 Convolution Kernels
    3. 8.3 Random Walk Graph Kernels
    4. 8.4 Path-Based Graph Kernels
    5. 8.5 Tree-Pattern Graph Kernels
    6. 8.6 Cyclic Pattern Kernels
    7. 8.7 Graphlet Kernels
    8. 8.8 Optimal Assignment Kernels
    9. 8.9 Other Graph Kernels
    10. 8.10 Applications in Bio-and Cheminformatics
    11. 8.11 Summary and Conclusions
    12. Acknowledgments
    13. References
  15. Chapter 9: Network-Based Information Synergy Analysis for Alzheimer Disease
    1. 9.1 Introduction
    2. 9.2 Datasets and Methods
    3. 9.3 Results
    4. 9.4 Summary and Conclusions
    5. Acknowledgment
    6. References
  16. Chapter 10: Density-Based Set Enumeration in Structured Data
    1. 10.1 Introduction
    2. 10.2 Unsupervised Pattern Discovery in Structured Data
    3. 10.3 Dense Cluster Enumeration in Weighted Interaction Networks
    4. 10.4 Dense Cluster Enumeration in Higher-Order Association Data
    5. 10.5 Discussion
    6. References
  17. Chapter 11: Hyponym Extraction Employing a Weighted Graph Kernel
    1. 11.1 Introduction
    2. 11.2 Related Work
    3. 11.3 Drawbacks of Current Approaches
    4. 11.4 Semantic Networks Following the MultiNet Formalism
    5. 11.5 Support Vector Machines and Kernels
    6. 11.6 Architecture
    7. 11.7 Graph Kernel
    8. 11.8 Graph Kernel Extensions
    9. 11.9 Distance Weighting
    10. 11.10 Features for Hyponymy Extraction
    11. 11.11 Evaluation
    12. 11.12 Conclusion and Outlook
    13. Acknowledgments
    14. References
  18. Index