yempane/g4ds

Graphs For Data Science

Code and slides to accompany the online series of webinars: https://data4sci.com/graphs-for-data-science by Data For Science.

Run the code in Binder:

Graphs are deceptively simple concepts: a set of individual Nodes (components) connected by Edges (relationships). In this very simplicity lies their power. They can describe the structure of our friendships, connections between airports, the spread of diseases from person to person, the relation of one concept to another, how species interact in an ecosystem or how computers communicate to form the World Wide Web.

Networks are the fundamental language of our increasingly complex world and the key to successfully understand it. By exploring in detail the way graphs can be used to explore, describe, analyze and understand empirical datasets we will put you at the forefront of this growing field.

In this tutorial we will use Python's NetworkX to build our understanding of network representations and algorithms by exploring real world network datasets such as the BTS airline transportation network, the BitCoin transaction network, the road network of OpenStreetMap, etc. Through this practical approach attendees will better understand the fundamental ideas and concepts that lie at the base of our increasingly complex world and take the first steps towards being at the forefront of this growing field.

Schedule

Airline Transportation Network – Weighted directed graphs

• Degrees and Weights
• Directed and Undirected Graphs
• Weight and Degree correlations

OpenStreetMap – Navigating graphs

• Breath First Search
• Depth First Search
• Dijkstra's Algorithm

BitCoin Transactions – Patterns and Structure in graphs

• Weakly Connected Graphs
• Strongly Connected Graphs
• Graph Motifs

Twitter – Social Structure

• Clustering
• Network Diameter
• Small World effect
• Community Structure

MovieLens Dataset - Recommendations and Link Prediction

• Bipartite Networks
• Network Decomposition
• Node Similarity
• Recommendations