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Complex network analysis in Python : recognize - construct - visualize - analyze - interpret / Dmitry Zinoviev ; edited by Adaobi Obi Tulton

Author
Zinoviev, Dmitry
Published
Raleigh, North Carolina : The Pragmatic Bookshelf, [2018]
Copyright Date
©2018
Physical Description
xvii, 233 pages : color illustrations ; 24 cm.
Additional Creators
Tulton, Adaobi Obi

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Series
Contents
Machine generated contents note: 1.The Art of Seeing Networks -- Know Thy Networks -- Enter Complex Network Analysis -- Draw Your First Network with Paper and Pencil -- pt. 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 -- pt. 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 -- pt. 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 -- pt. 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 -- pt. 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.
Summary
"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."--
Subject(s)
ISBN
9781680502695
1680502697
Reading Grade
Skill level [for] this book: Beginner [to intermediate].
Bibliography Note
Includes bibliographical references (pages [215]-217) and index.
Source of Acquisition
Purchased with funds from the James and Joyce Gettys Libraries Endowment in the Math Library and in the School of Information Sciences and Technology; 2017
Endowment Note
James and Joyce Gettys Libraries Endowment in the Math Library and in the School of Information Sciences and Technology
View MARC record | catkey: 23115597