Complex Network Analysis in Python : Recognize - Construct - Visualize - Analyze - Interpret

Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems Publisher's description Cover 1 Table of Contents 9 Acknowledgments 13 Preface 14 About the Reader 15 About the Book 15 About the Software 16 About the Notation 17 Online Resources 18 1. The Art of Seeing Networks 19 Know Thy Networks 20 Enter Complex Network Analysis 23 Draw Your First Network with Paper and Pencil 24 Part I—Elementary Networks and Tools 27 2. Surveying the Tools of the Craft 28 Do Not Weave Your Own Networks 28 Glance at iGraph 29 Appreciate the Power of graph-tool 30 Accept NetworkX 32 Keep in Mind NetworKit 32 Compare the Toolkits 33 3. Introducing NetworkX 34 Construct a Simple Network with NetworkX 34 Add Attributes 40 Visualize a Network with Matplotlib 42 Share and Preserve Networks 46 4. Introducing Gephi 48 Worth 1,000 Words 48 Import and Modify a Simple Network with Gephi 49 Explore the Network 51 Sketch the Network 53 Prepare a Presentation-Quality Image 55 Combine Gephi and NetworkX 57 5. Case Study: Constructing a Network of Wikipedia Pages 58 Get the Data, Build the Network 59 Eliminate Duplicates 62 Truncate the Network 63 Explore the Network 64 Part II—Networks Based on Explicit Relationships 67 6. Understanding Social Networks 68 Understand Egocentric and Sociocentric Networks 68 Recognize Communication Networks 76 Appreciate Synthetic Networks 78 Distinguish Strong and Weak Ties 81 7. Mastering Advanced Network Construction 83 Create Networks from Adjacency and Incidence Matrices 83 Work with Edge Lists and Node Dictionaries 90 Generate Synthetic Networks 92 Slice Weighted Networks 93 8. Measuring Networks 96 Start with Global Measures 96 Explore Neighborhoods 97 Think in Terms of Paths 101 Choose the Right Centralities 105 Estimate Network Uniformity Through Assortativity 110 9. Case Study: Panama Papers 114 Create a Network of Entities and Officers 114 Draw the Network 117 Analyze the Network 118 Build a ``Panama'' Network with Pandas 121 Part III—Networks Based on Co-Occurrences 125 10. Constructing Semantic and Product Networks 126 Semantic Networks 127 Product Networks 131 11. Unearthing the Network Structure 135 Locate Isolates 135 Split Networks into Connected Components 136 Separate Cores, Shells, Coronas, and Crusts 139 Extract Cliques 141 Recognize Clique Communities 144 Outline Modularity-Based Communities 146 Perform Blockmodeling 148 Name Extracted Blocks 149 12. Case Study: Performing Cultural Domain Analysis 150 Get the Terms 151 Build the Term Network 155 Slice the Network 156 Extract and Name Term Communities 157 Interpret the Results 159 13. Case Study: Going from Products to Projects 161 Read Data 161 Analyze the Networks 163 Name the Components 165 Part IV—Unleashing Similarity 169 14. Similarity-Based Networks 170 Understand Similarity 170 Choose the Right Distance 174 15. Harnessing Bipartite Networks 182 Work with Bipartite Networks Directly 183 Project Bipartite Networks 185 Compute Generalized Similarity 188 16. Case Study: Building a Network of Trauma Types 191 Embark on Psychological Trauma 191 Read the Data, Build a Bipartite Network 192 Build Four Weighted Networks 194 Plot and Compare the Networks 197 Part V—When Order Makes a Difference 200 17. Directed Networks 201 Discover Asymmetric Relationships 201 Explore Directed Networks 203 Apply Topological Sort to Directed Acyclic Graphs 207 Master ``toposort'' 208 A1. Network Construction, Five Ways 213 Pure Python 213 iGraph 214 graph-tool 215 NetworkX 216 NetworKit 216 A2. NetworkX 2.0 217 Bibliography 219 Index 222 – SYMBOLS – 222 – A – 222 – B – 222 – C – 223 – D – 225 – E – 226 – F – 227 – G – 227 – H – 228 – I – 228 – J – 228 – K – 228 – L – 229 – M – 229 – N – 230 – O – 231 – P – 232 – Q – 232 – R – 232 – S – 233 – T – 235 – U – 235 – V – 235 – W – 235 – Y – 236 – Z – 236 By Dmitry Zinoviev. Version: P1.0 (january 2018). Includes Bibliographical References.