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

Description

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. Cover Table of Contents Acknowledgments Preface About the Reader About the Book About the Software About the Notation Online Resources 1. The Art of Seeing Networks Know Thy Networks Enter Complex Network Analysis Draw Your First Network with Paper and Pencil Part I—Elementary Networks and Tools 2. Surveying the Tools of the Craft Do Not Weave Your Own Networks Glance at iGraph Appreciate the Power of graph-tool Accept NetworkX Keep in Mind NetworKit Compare the Toolkits 3. Introducing NetworkX Construct a Simple Network with NetworkX Add Attributes Visualize a Network with Matplotlib Share and Preserve Networks 4. Introducing Gephi Worth 1,000 Words Import and Modify a Simple Network with Gephi Explore the Network Sketch the Network Prepare a Presentation-Quality Image Combine Gephi and NetworkX 5. Case Study: Constructing a Network of Wikipedia Pages Get the Data, Build the Network Eliminate Duplicates Truncate the Network Explore the Network Part II—Networks Based on Explicit Relationships 6. Understanding Social Networks Understand Egocentric and Sociocentric Networks Recognize Communication Networks Appreciate Synthetic Networks Distinguish Strong and Weak Ties 7. Mastering Advanced Network Construction Create Networks from Adjacency and Incidence Matrices Work with Edge Lists and Node Dictionaries Generate Synthetic Networks Slice Weighted Networks 8. Measuring Networks Start with Global Measures Explore Neighborhoods Think in Terms of Paths Choose the Right Centralities Estimate Network Uniformity Through Assortativity 9. Case Study: Panama Papers Create a Network of Entities and Officers Draw the Network Analyze the Network Build a ``Panama'' Network with Pandas Part III—Networks Based on Co-Occurrences 10. Constructing Semantic and Product Networks Semantic Networks Product Networks 11. Unearthing the Network Structure Locate Isolates Split Networks into Connected Components Separate Cores, Shells, Coronas, and Crusts Extract Cliques Recognize Clique Communities Outline Modularity-Based Communities Perform Blockmodeling Name Extracted Blocks 12. Case Study: Performing Cultural Domain Analysis Get the Terms Build the Term Network Slice the Network Extract and Name Term Communities Interpret the Results 13. Case Study: Going from Products to Projects Read Data Analyze the Networks Name the Components Part IV—Unleashing Similarity 14. Similarity-Based Networks Understand Similarity Choose the Right Distance 15. Harnessing Bipartite Networks Work with Bipartite Networks Directly Project Bipartite Networks Compute Generalized Similarity 16. Case Study: Building a Network of Trauma Types Embark on Psychological Trauma Read the Data, Build a Bipartite Network Build Four Weighted Networks Plot and Compare the Networks Part V—When Order Makes a Difference 17. Directed Networks Discover Asymmetric Relationships Explore Directed Networks Apply Topological Sort to Directed Acyclic Graphs Master ``toposort'' A1. Network Construction, Five Ways Pure Python iGraph graph-tool NetworkX NetworKit A2. NetworkX 2.0 Bibliography Index – SYMBOLS – – A – – B – – C – – D – – E – – F – – G – – H – – I – – J – – K – – L – – M – – N – – O – – P – – Q – – R – – S – – T – – U – – V – – W – – Y – – Z –