Archive for the ‘NetworkX’ Category

Wikipedia in Python, Gephi, and Neo4j

Thursday, January 8th, 2015

Wikipedia in Python, Gephi, and Neo4j: Vizualizing relationships in Wikipedia by Matt Krzus.

From the introduction:

g3

We have had a bit of a stretch here where we used Wikipedia for a good number of things. From Doc2Vec to experimenting with word2vec layers in deep RNNs, here are a few of those cool visualization tools we’ve used along the way.

Cool things you will find in this post:

  • Building relationship links between Categories and Subcategories
  • Visualization with Networkx (think Betweenness Centrality and PageRank)
  • Neo4j and Cypher (the author thinks avoiding the Giraph learning curve is a plus, I leave that for you to decide)
  • Visualization with Gephi

Enjoy!

JSNetworkX

Wednesday, March 13th, 2013

JSNetworkX

A port of the NetworkX graph library to JavaScript

SNetworkX is a port of the popular Python graph library NetworkX. Lets describe it with their words:

NetworkX is a Python language software package for the creation, manipulation, and study of the structure, dynamics, and function of complex networks.

With NetworkX you can load and store networks in standard and nonstandard data formats, generate many types of random and classic networks, analyze network structure, build network models, design new network algorithms, draw networks, and much more.

Github.

Wiki.

Looks like an easy way to include graph representations of topic maps in a web page.

I suspect you will be seeing more of this in the not too distant future.

I first saw this in a tweet by Christophe Viau.

NetworkX – 1.6

Tuesday, November 22nd, 2011

NetworkX – 1.6

NetworkX released a new version today.

From the home page:

NetworkX is a Python language software package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

Features:

  • Python language data structures for graphs, digraphs, and multigraphs.
  • Nodes can be “anything” (e.g. text, images, XML records)
  • Edges can hold arbitrary data (e.g. weights, time-series)
  • Generators for classic graphs, random graphs, and synthetic networks
  • Standard graph algorithms
  • Network structure and analysis measures
  • Basic graph drawing
  • Open source BSD license
  • Well tested: more than 1500 unit tests
  • Additional benefits from Python: fast prototyping, easy to teach, multi-platform

That list of features seems quite different from when I first covered it in January of this year.

Matthew O’Donnell tweeted about it.