Abstract

Have you ever wondered about how those data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this tutorial is for you. In this tutorial, we will use a variety of datasets to help you understand the fundamentals of network thinking, with a particular focus on constructing, summarizing, and visualizing complex networks.

Audience

This tutorial is for Pythonistas who want to understand relationship problems - as in, data problems that involve relationships between entities. Participants should already have a grasp of for loops and basic Python data structures (lists, tuples and dictionaries). By the end of the tutorial, participants will have learned how to use the NetworkX package in the Jupyter environment, and will become comfortable in visualizing large networks using Circos plots. Other plots will be introduced as well.

Outline

Part 1: Introduction (30 min)

• Networks of all kinds: biological, transportation.
• Representation of networks, NetworkX data structures
• Basic quick-and-dirty visualizations

Part 2: Hubs and Paths (40 min)

• Finding important nodes; applications
• Pathfinding algorithms and their applications
• Hands-on: implementing path-finding algorithms
• Visualize degree and betweenness centrality distributions.

Part 3: Cliques, Triangles & Structures (40 min)

• Definition of cliques
• Triangles as the simplest complex clique, applications
• Using path-finding algorithms to find structures in a graph.
• Open triangles as recommender systems.

Part 4: Advanced Network Visualizations (40 min)

• Basic concepts in rational layouts: node positioning, node colouring.
• Plots: Circos, Arc, Hive, Matrix, Flow plots

Part 5: Bipartite Graphs (30 min)

• Definition of bipartite graphs, applications
• Constructing bipartite graphs in NetworkX.
• Summary statistics of bipartite graphs
• Double-Arc plots for visualization