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README.rst

Social Network Analysis with Python and NetworkX

Materials for the PyData Barcelona 2017 workshop on Network Analysis with Python and NetworkX.

Abstract

Social Network Analysis (SNA) has a wide applicability in many scientific fields and industries. This workshop is a gentle introduction to SNA using Python and NetworkX, a powerful and mature python library for the study of the structure, dynamics, and functions of complex networks. Participants in this workshop should have a basic understanding of Python, no previous knowledge of SNA is assumed.

For this workshop attendees will need to install NetworkX (>=1.11), Matplotlib (>=1.5), numpy (>=1.10) and have a working Jupyter Notebook environment. Some examples will also use Pandas (>=0.17) and Seaborn (>=0.7), but these packages are not essential. Only basic Python knowledge is assumed.

Outline of the workshop

  1. Brief Introduction to Graph Theory
    • Mathematical foundation of Social Network Analysis.
    • Why graphical representations usually doesn't help much.
  2. Creating and Manipulating Graphs
    • Data Structures: Graphs, DiGraphs, MultiGraphs and MultiDiGraphs.
    • Adding nodes and edges.
    • Adding and updating node and edge attributes.
    • Graph generators.
    • Visualizing graphs using Matplotlib.
    • Common formats for reading and writing Graphs.
  3. Network Analysis
    • Basic concepts: Degree.
    • Distance measures: paths, simple paths, and shortest paths.
    • Node centrality analysis: measures and their relation.
    • Analyzing groups and subgroups: Cliques, k-cores, components, and k-components.
  4. Bipartite Graphs
    • Definition of bipartite networks and their use in modeling group affiliations.
    • Working with bipartite networks with NetworkX.

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Materials for the NetworkX workshop at PyData Barcelona 2017 conference

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