Mining Complex Networks

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