Statistical and machine learning approaches for network analysis
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...
Uložené v:
| Hlavní autori: | , |
|---|---|
| Médium: | E-kniha Kniha |
| Jazyk: | English |
| Vydavateľské údaje: |
Hoboken, N.J
Wiley
2012
John Wiley & Sons, Incorporated Wiley-Blackwell |
| Vydanie: | 1 |
| Edícia: | Wiley Series in Computational Statistics |
| Predmet: | |
| ISBN: | 9780470195154, 0470195150, 111834698X, 9781118346983, 1118346998, 9781118346990 |
| On-line prístup: | Získať plný text |
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- Intro -- 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
- 11.10 Features for Hyponymy Extraction -- 11.11 Evaluation -- 11.12 Conclusion and Outlook -- Acknowledgments -- References -- Index
- 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
- Statistical and Machine Learning Approaches for Network Analysis -- Contents -- Preface -- Contributors -- 1 A Survey of Computational Approaches to Reconstruct and Partition Biological Networks -- 1.1 INTRODUCTION -- 1.2 BIOLOGICAL NETWORKS -- 1.2.1 Directed Networks -- 1.2.2 Undirected Networks -- 1.3 GENOME-WIDE MEASUREMENTS -- 1.3.1 Gene Expression Data -- 1.3.2 Gene Sets -- 1.4 RECONSTRUCTION OF BIOLOGICAL NETWORKS -- 1.4.1 Reconstruction of Directed Networks -- 1.4.1.1 Boolean Networks -- 1.4.1.2 Probabilistic Boolean Networks -- 1.4.1.3 Bayesian Networks -- 1.4.1.4 Collaborative Graph Model -- 1.4.1.5 Frequency Method -- 1.4.1.6 EM-Based Inference from Gene Sets -- 1.4.2 Reconstruction of Undirected Networks -- 1.4.2.1 Relevance Networks -- 1.4.2.2 Graphical Gaussian Models -- 1.5 PARTITIONING BIOLOGICAL NETWORKS -- 1.5.1 Directed and Undirected Networks -- 1.5.2 Partitioning Undirected Networks -- 1.5.2.1 Kernighan-Lin Algorithm -- 1.5.2.2 Girvan-Newman Algorithm -- 1.5.2.3 Newman's Eigenvector Method -- 1.5.2.4 Infomap -- 1.5.2.5 Clique Percolation Method -- 1.5.3 Partitioning Directed Networks -- 1.5.3.1 Newman's Eigenvector Method -- 1.5.3.2 Infomap -- 1.5.3.3 Clique Percolation Method -- 1.6 DISCUSSION -- REFERENCES -- 2 Introduction to Complex Networks: Measures, Statistical Properties, and Models -- 2.1 INTRODUCTION -- 2.2 REPRESENTATION OF NETWORKS -- 2.3 CLASSICAL NETWORK -- 2.3.1 Random Network -- 2.3.2 Lattice Network -- 2.4 SCALE-FREE NETWORK -- 2.4.1 Degree Distribution -- 2.4.2 Degree Distribution of Random Network -- 2.4.3 Power-Law Distribution in Real-World Networks -- 2.4.4 Barab´asi-Albert Model -- 2.4.5 Configuration Model -- 2.5 SMALL-WORLD NETWORK -- 2.5.1 Average Shortest Path Length -- 2.5.2 Ultrasmall-World Network -- 2.6 CLUSTERED NETWORK -- 2.6.1 Clustering Coefficient -- 2.6.2 Watts-Strogatz Model
- 8.2.2 Variants and Extensions -- 8.3 RANDOM WALK GRAPH KERNELS -- 8.3.1 Definition -- 8.3.2 Computation -- 8.3.3 Variants and Extensions -- 8.4 PATH-BASED GRAPH KERNELS -- 8.4.1 Definition and Computation -- 8.4.2 Variants and Extensions -- 8.5 TREE-PATTERN GRAPH KERNELS -- 8.5.1 Definition -- 8.5.2 Computation -- 8.5.3 Variants and Extensions -- 8.6 CYCLIC PATTERN KERNELS -- 8.6.1 Definition -- 8.6.2 Computation -- 8.6.3 Variants and Extensions -- 8.7 GRAPHLET KERNELS -- 8.7.1 Definition -- 8.7.2 Computation -- 8.7.3 Variants and Extensions -- 8.8 OPTIMAL ASSIGNMENT KERNELS -- 8.8.1 Definition -- 8.8.2 Computation -- 8.8.3 Variants and Extensions -- 8.9 OTHER GRAPH KERNELS -- 8.10 APPLICATIONS IN BIO- AND CHEMINFORMATICS -- 8.11 SUMMARY AND CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- 9 Network-Based Information Synergy Analysis for Alzheimer Disease -- 9.1 INTRODUCTION -- 9.2 DATASETS AND METHODS -- 9.2.1 Microarray Dataset and Protein-Protein Interaction Data -- 9.2.2 Calculation of the Synergy Scores of Gene Pairs -- 9.2.3 Permutation Test to Evaluate the Significance of the Synergy -- 9.2.4 Characterization of the Network Topology -- 9.3 RESULTS -- 9.3.1 Information Synergy for Simulated Gene Pairs -- 9.3.2 Topological Characteristics of Synergy Network for AD -- 9.3.3 Differential Expression and Correlation Patterns for the Pairs in Synergy Network -- 9.3.4 Hub Genes in the Positive Synergy Network -- 9.4 SUMMARY AND CONCLUSIONS -- ACKNOWLEDGMENT -- REFERENCES -- 10 Density-Based Set Enumeration in Structured Data -- 10.1 INTRODUCTION -- 10.2 UNSUPERVISED PATTERN DISCOVERY IN STRUCTURED DATA -- 10.2.1 Graph Mining -- 10.2.2 Optimal Subgraph Search -- 10.2.3 Graph Clustering -- 10.2.4 Bicluster Analysis -- 10.2.5 Itemset Mining -- 10.2.6 Relational Data Mining and Higher-Order Association Analysis
- 10.3 DENSE CLUSTER ENUMERATION IN WEIGHTED INTERACTION NETWORKS -- 10.3.1 Notation and Problem Definition -- 10.3.2 Enumeration Algorithm -- 10.3.2.1 Search Space -- 10.3.2.2 Reduction Scheme -- 10.3.2.3 Search Procedure -- 10.3.3 Efficient Generation of Children -- 10.3.4 Complexity -- 10.3.5 Output Representation -- 10.3.5.1 Locally Maximal Clusters -- 10.3.5.2 Cluster Ranking -- 10.3.6 Degree-Based Cluster Criteria -- 10.3.6.1 Minimum Degree -- 10.3.7 Minimum Relative Degree and Quasi-Cliques -- 10.3.7.1 Approaches to Quasi-Clique Mining -- 10.3.8 Integration of Node Weights -- 10.3.9 Constraint Integration -- 10.3.9.1 Constraints from External Data Sources -- 10.3.9.2 Connectivity Constraints -- 10.3.9.3 Cardinality and Branching Restrictions -- 10.4 DENSE CLUSTER ENUMERATION IN HIGHER-ORDER ASSOCIATION DATA -- 10.4.1 Motivation -- 10.4.2 Problem Definition -- 10.4.3 Enumeration Approach -- 10.4.3.1 Global Index Representation -- 10.4.3.2 Search Space -- 10.4.3.3 Reduction Scheme -- 10.4.3.4 Search Algorithm -- 10.4.3.5 Symmetry Adaptations -- 10.5 DISCUSSION -- REFERENCES -- 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
- 4.3.3 On Network Entropy -- 4.4 DISCUSSION AND CONCLUDING REMARKS -- ACKNOWLEDGMENT -- SUPPORTING INFORMATION -- REFERENCES -- 5 Influence of Statistical Estimators on the Large-Scale Causal Inference of Regulatory Networks -- 5.1 INTRODUCTION -- 5.2 METHODS -- 5.2.1 C3NET -- 5.2.1.1 C3NET (Conservative Causal Core) -- 5.2.2 Estimating Mutual Information -- 5.2.3 Global Measures -- 5.2.4 Ensemble Data and Local Network-Based Measures -- 5.2.5 Local Network-Based Measures -- 5.2.6 Network Structure -- 5.3 RESULTS -- 5.3.1 Global Network Inference Performance -- 5.3.2 Local Network Inference Performance -- 5.4 CONCLUSION AND SUMMARY -- ACKNOWLEDGMENT -- REFERENCES -- 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 -- 6.4.1 Characterization -- 6.4.2 Generation -- 6.4.3 Observations -- 6.5 COMPARING TOPOLOGY GENERATORS -- 6.5.1 Methodology -- 6.5.2 Topological Metrics -- 6.5.3 Discussion -- 6.6 TUNING TOPOLOGY GENERATOR PARAMETERS -- 6.6.1 Link Densities -- 6.6.2 Spectra PDF -- 6.6.3 Limitations of Spectra PDF -- 6.6.4 Weighted Spectra -- 6.6.5 Weighted Spectra Comparison -- 6.7 GENERATING TOPOLOGIES WITH OPTIMUM PARAMETERS -- 6.8 INTERNET TOPOLOGY EVOLUTION -- 6.9 CONCLUSIONS -- REFERENCES -- 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 -- 8 Graph Kernels -- 8.1 INTRODUCTION -- 8.1.1 Kernel Learning in a Nutshell -- 8.1.2 Graph Kernels -- 8.1.3 Computational Considerations -- 8.2 CONVOLUTION KERNELS -- 8.2.1 Definition
- 2.7 HIERARCHICAL MODULARITY -- 2.7.1 Hierarchical Model -- 2.7.2 Dorogovtsev-Mendes-Samukhin Model -- 2.8 NETWORK MOTIF -- 2.9 ASSORTATIVITY -- 2.9.1 Assortative Coefficient -- 2.9.2 Degree Correlation -- 2.9.3 Linear Preferential Attachment Model -- 2.9.4 Edge Rewiring Method -- 2.10 RECIPROCITY -- 2.11 WEIGHTED NETWORKS -- 2.11.1 Strength -- 2.11.2 Weighted Clustering Coefficient -- 2.11.3 Weighted Degree Correlation -- 2.12 NETWORK COMPLEXITY -- 2.13 CENTRALITY -- 2.13.1 Definition -- 2.13.2 Comparison of Centrality Measures -- 2.14 CONCLUSION -- REFERENCES -- 3 Modeling for Evolving Biological Networks -- 3.1 INTRODUCTION -- 3.2 UNIFIED EVOLVING NETWORK MODEL: REPRODUCTION OF HETEROGENEOUS CONNECTIVITY, HIERARCHICAL MODULARITY, AND DISASSORTATIVITY -- 3.2.1 Network Model -- 3.2.2 Degree Distribution -- 3.2.3 Degree-Dependent Clustering Coefficient -- 3.2.4 Average Clustering Coefficient -- 3.2.5 Degree Correlation -- 3.2.6 Assortative Coefficient -- 3.2.7 Comparison with Real Data -- 3.3 MODELING WITHOUT PARAMETER TUNING: A CASE STUDY OF METABOLIC NETWORKS -- 3.3.1 Network Model -- 3.3.2 Analytical Solution -- 3.3.3 Estimation of the Parameters -- 3.3.4 Comparison with Real Data -- 3.4 BIPARTITE RELATIONSHIP: A CASE STUDY OF METABOLITE DISTRIBUTION -- 3.4.1 Structural Properties of Metabolite Distributions -- 3.4.2 Bipartite Network Model -- 3.4.3 Comparison with Real Data -- 3.4.4 Related Model -- 3.5 CONCLUSION -- REFERENCES -- 4 Modularity Configurations in Biological Networks with Embedded Dynamics -- 4.1 INTRODUCTION -- 4.1.1 Biological Networks and Computational Challenges -- 4.1.2 Outline -- 4.2 METHODS -- 4.2.1 General Approach -- 4.2.2 PIN Fragmentation -- 4.2.3 PIN Topology -- 4.2.3.1 Modularity by Communities -- 4.2.3.2 Modularity by Cores -- 4.3 RESULTS -- 4.3.1 Community Maps -- 4.3.1.1 Analysis -- 4.3.2 Core Structures