Exploring the Interconnected World: Network/Graph Analysis in Python
This course is available only as a part of subscription plans
Training duration: 4 hr (Hands-on)
Understand the basics of graphs/networks properties and analysis, including what can you use it for and how
Learn how to generate basic network types, and the most often encountered network models in real data. Next, discover the most informative network measures to understand network structures and behaviors
Extract and interpret information about real public social network data by building, analyzing and visualizing it to gain understanding about its structure and behaviors
Matt Brems
Noemi Derzsy, PhD
Module 1: The Unreasonable Effectiveness of Deep Learning
● Training Overview
● A Brief History from Graph Theory to Network Science
● Real-World Applications of Networks/Graphs Overview
● Basic Network Structural Properties
● Graphs in Python with NetworkX
Module 2: Generate & manipulate graph structures
● Create, modify and delete graphs
● Node, edge properties and structure
● Create graph structure from datafile
● Weighted graphs
● Directed graphs
● Multigraphs
● Bipartite graphs
Module 3: Analyze and visualize networks
● Structural properties analysis
● Node degree, average degree, degree distribution
● Clustering, coefficient, triangles
● Centrality measures
● Components
● Assortativity
● Network visualization with NetworkX
● Network visualization with nxviz
● Visualize subgraphs
● Network visualization with node attributes
Basic Python
Jupyter Notebooks
Installation of NetworkX package