| Preface |
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v | |
| About the Author |
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ix | |
| Acknowledgments |
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xi | |
| Part 1 Mathematical Fundamentals |
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1 | (102) |
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1 Fundamentals of Graph Theory |
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3 | (18) |
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1.1 Definitions and Concepts |
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3 | (11) |
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1.2 Topological Definition of Graph |
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14 | (7) |
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21 | (42) |
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2.1 Matrix Representation of Graphs |
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21 | (9) |
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2.2 Computer Storage of Graph |
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30 | (2) |
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32 | (31) |
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3 Fundamentals of Network Theory |
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63 | (18) |
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64 | (2) |
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3.2 Random and Complex Networks |
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66 | (4) |
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70 | (1) |
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71 | (10) |
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81 | (22) |
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81 | (2) |
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83 | (1) |
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4.3 Randomization, Bootstrap, and Monte Carlo Methods |
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84 | (6) |
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90 | (1) |
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90 | (4) |
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94 | (4) |
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98 | (1) |
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98 | (5) |
| Part 2 Crucial Nodes/Subnetworks/Modules, Network Types, and Structural Comparison |
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103 | (52) |
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5 Identification of Crucial Nodes and Subnetworks/Modules |
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105 | (24) |
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5.1 Features of Crucial Nodes |
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105 | (1) |
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5.2 Indices and Methods of Crucial Nodes |
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106 | (11) |
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117 | (1) |
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118 | (3) |
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5.5 Identification of Subnetworks/Modules |
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121 | (8) |
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6 Detection of Network Types |
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129 | (10) |
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130 | (5) |
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135 | (4) |
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7 Comparison of Network Structure |
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139 | (16) |
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7.1 Nonparametric Statistic Comparison of Network Structure |
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139 | (7) |
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7.2 Nonparametric Statistic Comparison of Community Structure |
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146 | (6) |
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7.3 Network Matrix-based Methods |
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152 | (2) |
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154 | (1) |
| Part 3 Network Dynamics, Evolution, Simulation, and Control |
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155 | (108) |
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157 | (18) |
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8.1 Differential Equations and Motion Stability |
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157 | (7) |
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8.2 Dynamics of Some Networks |
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164 | (11) |
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9 Network Robustness and Sensitivity Analysis |
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175 | (20) |
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175 | (10) |
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185 | (10) |
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195 | (8) |
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10.1 Conventional Control |
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195 | (6) |
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10.2 New Perspectives of Network Control |
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201 | (2) |
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203 | (26) |
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11.1 A Generalized Network Evolution Model for Community Assembly Dynamics |
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203 | (11) |
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11.2 A Model for Perturbed Food Web Dynamics |
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214 | (1) |
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11.3 A Network Evolution Algorithm Based on Node Attraction |
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215 | (6) |
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11.4 Phase Recognition of Network Evolution |
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221 | (8) |
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229 | (8) |
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12.1 Classification of Cellular Automata |
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230 | (2) |
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12.2 Algorithms of Cellular Automata |
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232 | (1) |
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12.3 Some Cases of Cellular Automata |
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232 | (5) |
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237 | (18) |
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13.1 Basic Concept of Self-Organization |
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237 | (2) |
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13.2 Theories and Principles of Self-Organization |
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239 | (10) |
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13.3 Existing Algorithms of Self-Organization |
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249 | (1) |
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13.4 Self-Organization in Life Sciences |
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249 | (3) |
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252 | (3) |
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255 | (8) |
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255 | (3) |
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14.2 Principle and Methods of Agent-based Modeling |
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258 | (5) |
| Part 4 Flow Analysis |
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263 | (28) |
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265 | (26) |
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15.1 Flux Balance Analysis |
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265 | (18) |
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15.2 Flow Indices for Ecological Network Analysis |
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283 | (8) |
| Part 5 Link and Node Prediction |
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291 | (74) |
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16 Link Prediction: Sampling-based Methods |
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293 | (28) |
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16.1 Linear Correlation Analysis for Finding Interactions |
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293 | (8) |
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16.2 Partial Correlation of General Correlation Measures |
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301 | (12) |
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16.3 Combined Use of Linear Correlation and Rank Correlation: A Hierarchical Method for Finding Interactions |
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313 | (8) |
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17 Link Prediction: Structure- and Perturbation-based Methods |
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321 | (24) |
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322 | (17) |
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17.2 Performance Assessment of Link Prediction Methods |
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339 | (6) |
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18 Link Prediction: Node-Similarity-based Methods |
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345 | (16) |
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18.1 Link Prediction Based on Node Similarity |
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345 | (6) |
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18.2 Screening Node Attributes that Significantly Influence Node Centrality |
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351 | (10) |
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361 | (4) |
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361 | (2) |
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363 | (2) |
| Part 6 Network Construction |
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365 | (24) |
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20 Construction of Biological Networks |
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367 | (22) |
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20.1 Prediction Methods of Protein-Protein Interactions |
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367 | (3) |
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20.2 Gene Coexpression Network Analysis |
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370 | (2) |
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20.3 Classification-based Machine Learning |
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372 | (2) |
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374 | (2) |
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20.5 Homogeneity Test of Samples and Sampling Completeness |
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376 | (5) |
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20.6 Network Construction Based on Power-Law or Exponential-Law Distribution of Node Degree |
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381 | (8) |
| Part 7 Pharmacological and Toxicological Networks |
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389 | (24) |
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21 Network Pharmacology and Toxicology |
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391 | (22) |
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21.1 Network Pharmacology |
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391 | (13) |
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404 | (9) |
| Part 8 Ecological Networks |
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413 | (26) |
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415 | (24) |
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22.1 Fundamentals of Food Webs |
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416 | (6) |
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422 | (13) |
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435 | (4) |
| Part 9 Microscopic Networks |
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439 | (20) |
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23 Molecular and Cellular Networks |
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441 | (18) |
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23.1 Metabolic Pathway of Nonalcoholic Fatty Liver Disease |
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442 | (2) |
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23.2 E. coli Transcriptional Regulatory Network |
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444 | (3) |
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23.3 Common Disease Regulatory Network for Metabolic Disorders |
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447 | (2) |
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23.4 Find Molecular Pathological Events in Different Cancer Tissues |
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449 | (3) |
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23.5 Crucial Metabolites/Reactions in Tumor Signaling Networks |
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452 | (1) |
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453 | (6) |
| Part 10 Social Networks |
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459 | (10) |
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24 Social Network Analysis |
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461 | (8) |
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24.1 Definition of Social Networks |
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461 | (6) |
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467 | (2) |
| Part 11 Software |
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469 | (20) |
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25 Software for Network Analysis |
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471 | (18) |
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25.1 Software for Comprehensive Network Analysis |
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471 | (14) |
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25.2 Software for Network Layout and Others |
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485 | (4) |
| Part 12 Big Data Analytics |
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489 | (22) |
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26 Big Data Analytics for Network Biology |
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491 | (20) |
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26.1 Fundamentals of Big Data Analytics |
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492 | (4) |
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26.2 Text Mining with Python and R |
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496 | (3) |
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26.3 Big Data Analytics of Medicinal Attributes and Functions of Chinese Herbal Medicines |
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499 | (12) |
| References |
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511 | (32) |
| Index |
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543 | |
Lisainfo e-raamatute kohta