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Complex Network Analysis in Python

Recognize> Construct> Visualize> Analyze> Interpret

Paperback Engels 2018 9781680502695
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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.

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.


Aantal pagina's:233
Hoofdrubriek:IT-management / ICT


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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 PagesCase 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 NetworksConstructing 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 AnalysisCase 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 ProjectsCase 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 TypesCase 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

A2. NetworkX 2.0

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