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.

Table of Contents

Chapter 1. The Art Of Seeing Networks

Part I. Elementary Networks and Tools
Chapter 2. Surveying The Tools Of The Craft
Chapter 3. Introducing Networkx
Chapter 4. Introducing Gephi
Chapter 5. Case Study: Constructing A Network Of Wikipedia Pages

Part II. Networks Based on Explicit Relationships
Chapter 6. Understanding Social Networks
Chapter 7. Mastering Advanced Network Construction
Chapter 8. Measuring Networks
Chapter 9. Case Study: Panama Papers

Part III. Networks Based on Co-Occurrences
Chapter 10. Constructing Semantic And Product Networks
Chapter 11. Unearthing The Network Structure
Chapter 12. Case Study: Performing Cultural Domain Analysis
Chapter 13. Case Study: Going From Products To Projects

Part IV. Unleashing Similarity
Chapter 14. Similarity-Based Networks
Chapter 15. Harnessing Bipartite Networks
Chapter 16. Case Study: Building A Network Of Trauma Types

Part V. When Order Makes a Difference
Chapter 17. Directed Networks