Mining Complex Networks
- Length: 280 pages
- Edition: 1
- Language: English
- Publisher: Chapman and Hall/CRC
- Publication Date: 2021-12-07
- ISBN-10: 1032112034
- ISBN-13: 9781032112039
- Sales Rank: #0 (See Top 100 Books)
This book concentrates on mining networks, a subfield within data science. Data science uses scientific and computational tools to extract valuable knowledge from large data sets. Once data is processed and cleaned, it is analyzed and presented to support decision-making processes. Data science and machine learning tools have become widely used in companies of all sizes.
Networks are often large-scale, decentralized, and evolve dynamically over time. Mining complex networks aim to understand the principles governing the organization and the behavior of such networks is crucial for a broad range of fields of study. Here are a few selected typical applications of mining networks:
- Community detection (which users on some social media platforms are close friends).
- Link prediction (who is likely to connect to whom on such platforms).
- Node attribute prediction (what advertisement should be shown to a given user of a particular platform to match their interests).
- Influential node detection (which social media users would be the best ambassadors of a specific product).
This textbook is suitable for an upper-year undergraduate course or a graduate course in programs such as data science, mathematics, computer science, business, engineering, physics, statistics, and social science. This book can be successfully used by all enthusiasts of data science at various levels of sophistication to expand their knowledge or consider changing their career path.
Jupiter notebooks (in Python and Julia) accompany the book and can be accessed on https://www.ryerson.ca/mining-complex-networks/. These not only contain all the experiments presented in the book, but also include additional material.
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
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