Bipartite graphs can be represented using another type of matrix. Bipartite graphs have two types of vertices, which I'll call row-vertices and column-vertices, for reasons that will become obvious. All edges connect one row-vertex to one column-vertex, so it's not necessary to use a full adjacency matrix connecting all possible vertex pairs. Instead, we represent the edge from the ith row-vertex to the jth column-vertex by setting the element of the matrix at row i and column j. This type of matrix is called a biadjacency matrix, and is typically denoted as B. Because the number of row vertices and column vertices can be different, the biadjacency matrix does not need to be square. The bipartite graph can be projected into a graph containing only row-nodes (or only column-nodes) by using simple matrix operations.
Network Science with Python and NetworkX Quick Start Guide
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Network Science with Python and NetworkX Quick Start Guide
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Overview of this book
NetworkX is a leading free and open source package used for network science with the Python programming language. NetworkX can track properties of individuals and relationships, find communities, analyze resilience, detect key network locations, and perform a wide range of important tasks. With the recent release of version 2, NetworkX has been updated to be more powerful and easy to use.
If you’re a data scientist, engineer, or computational social scientist, this book will guide you in using the Python programming language to gain insights into real-world networks. Starting with the fundamentals, you’ll be introduced to the core concepts of network science, along with examples that use real-world data and Python code. This book will introduce you to theoretical concepts such as scale-free and small-world networks, centrality measures, and agent-based modeling. You’ll also be able to look for scale-free networks in real data and visualize a network using circular, directed, and shell layouts.
By the end of this book, you’ll be able to choose appropriate network representations, use NetworkX to build and characterize networks, and uncover insights while working with real-world systems.
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Current Title:
Network Science with Python and NetworkX Quick Start Guide
Table of Contents (15 chapters)
Preface
Free Chapter
What is a Network?
Working with Networks in NetworkX
From Data to Networks
Affiliation Networks
The Small Scale - Nodes and Centrality
The Big Picture - Describing Networks
In-Between - Communities
Social Networks and Going Viral
Simulation and Analysis
Networks in Space and Time
Visualization
Conclusion
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