Mining Complex Networks

Description

Cover Half Title Title Page Copyright Page Contents Preface I. Core Material 1. Graph Theory 1.1. Notation 1.2. Probability 1.3. Linear Algebra 1.4. Definition 1.5. Adjacency Matrix 1.6. Weighted Graphs 1.7. Connected Components and Distances 1.8. Degree Distribution 1.9. Subgraphs 1.10. Special Families 1.11. Clustering Coefficient 1.12. Experiments 1.13. Practitioner's Corner 1.14. Problems 1.15. Recommended Supplementary Reading 2. Random Graph Models 2.1. Introduction 2.2. Asymptotic Notation 2.3. Binomial Random Graphs 2.4. Power-Law Degree Distribution 2.5. Chung-Lu Model 2.6. Random d-regular Graphs 2.7. Random Graphs with a Given Degree Sequence 2.8. Experiments 2.9. Practitioner's Corner 2.10. Problems 2.11. Recommended Supplementary Reading 3. Centrality Measures 3.1. Introduction 3.2. Matrix Based Measures 3.3. Distance Based Measures 3.4. Analyzing Centrality Measures 3.5. Pruning Unimportant Nodes, k-cores 3.6. Group Centrality and Graph Centralization 3.7. Experiments 3.8. Practitioner's Corner 3.9. Problems 3.10. Recommended Supplementary Reading 4. Degree Correlations 4.1. Introduction 4.2. Assortativity and Disassortativity 4.3. Measures of Degree Correlations 4.4. Structural Cut-offs 4.5. Correlations in Directed Graphs 4.6. Implications for Other Graph Parameters 4.7. Experiments 4.8. Practitioner's Corner 4.9. Problems 4.10. Recommended Supplementary Reading 5. Community Detection 5.1. Introduction 5.2. Basic Properties of Communities 5.3. Synthetic Models with Community Structure 5.4. Graph Modularity 5.5. Hierarchical Clustering 5.6. A Few Other Methods 5.7. Experiments 5.8. Practitioner's Corner 5.9. Problems 5.10. Recommended Supplementary Reading 6. Graph Embeddings 6.1. Introduction 6.2. Problem Formalization 6.3. Techniques 6.4. Unsupervised Benchmarking Framework 6.5. Applications 6.6. Other Directions 6.7. Experiments 6.8. Practitioner's Corner 6.9. Problems 6.10. Recommended Supplementary Reading 7. Hypergraphs 7.1. Introduction 7.2. Basic Definitions 7.3. Random Hypergraph Models 7.4. Community Detection in Hypergraphs 7.5. Experiments 7.6. Practitioner's Corner 7.7. Problems 7.8. Recommended Supplementary Reading II. Additional Material 8. Detecting Overlapping Communities 8.1. Overlapping Cliques 8.2. Ego-splitting 8.3. Edge Clustering 8.4. Illustration: Word Association Graph 8.5. Benchmark Graphs 8.6. Recommended Supplementary Reading 9. Embedding Graphs 9.1. NCI1 and NCI109 Datasets 9.2. Supervised Learning with Embedded Graphs 9.3. Unsupervised Learning 9.4. Recommended Supplementary Reading 10. Network Robustness 10.1. Power Grid Network on the Iberian Peninsula 10.2. Synthetic Networks 10.3. Conclusion 10.4. Recommended Supplementary Reading 11. Road Networks 11.1. Representing a Road Network as a Graph 11.2. Identifying Busy Intersections 11.3. Recommended Supplementary Reading Index