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You're reading from  Network Science with Python and NetworkX Quick Start Guide

Product typeBook
Published inApr 2019
Reading LevelIntermediate
PublisherPackt
ISBN-139781789955316
Edition1st Edition
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Edward L. Platt
Edward L. Platt
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Edward L. Platt

Edward L. Platt creates technology for communities and communities for technology. He is currently a researcher at the University of Michigan School of Information and the Center for the Study of Complex Systems. He has published research on large-scale collective action, social networks, and online communities. He was formerly a staff researcher at the MIT Center for Civic Media. He contributes to many free/open source software projects, including tools for media analysis, network science, and cooperative organizations. He has also done research on quantum computing and fault tolerance. He has an M.Math in Applied Mathematics from the University of Waterloo, as well as B.S degrees in both Computer Science and Physics from MIT.
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Adding attributes to nodes and edges

In the last chapter, I said that networks were entirely defined by the number of nodes and which nodes were connected. I lied. Kind of. Now that we're all a little older and wiser than we were in Chapter 1, What is a Network?, I can tell you the whole truth: sometimes, network nodes and edges are annotated with additional information. In the Graph class, each node and edge can have a set of attributes to store this additional information. Attributes can simply be a convenient place to store information related to the nodes and edges, or they can be used by visualizations and network algorithms.

The Graph class allows you to add any number of attributes to a node. For a G, network, each node's attributes are stored in the dict at G.nodes[v], where v is the node's ID. In the karate club example, the club members eventually split...

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Network Science with Python and NetworkX Quick Start Guide
Published in: Apr 2019Publisher: PacktISBN-13: 9781789955316

Author (1)

author image
Edward L. Platt

Edward L. Platt creates technology for communities and communities for technology. He is currently a researcher at the University of Michigan School of Information and the Center for the Study of Complex Systems. He has published research on large-scale collective action, social networks, and online communities. He was formerly a staff researcher at the MIT Center for Civic Media. He contributes to many free/open source software projects, including tools for media analysis, network science, and cooperative organizations. He has also done research on quantum computing and fault tolerance. He has an M.Math in Applied Mathematics from the University of Waterloo, as well as B.S degrees in both Computer Science and Physics from MIT.
Read more about Edward L. Platt