Hands-On Network Science, PyData Global 2023
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The document presents an overview of applying network science to various problems, including epidemic spread, stock market prediction, and food pricing analysis. It discusses the use of mathematical principles such as topology and geometry, alongside network structures to enhance data visualization and computational feasibility. The document also highlights case studies demonstrating the effectiveness of network science methods over traditional approaches in analyzing complex relationships in data.
Hands-On Network Science, PyData Global 2023
- 1. Hands-On Network Science Colleen M. Farrelly, Post Urban Ventures Yae Ulrich Gaba, Quantum Leap Africa Franck Kalala Mutumbo, University of Lubumbashi
- 2. • Set with defined relationships across items in the set • Examples: • People connected to each other on social media • Geographic areas connected by animal migration patterns • Stocks connected by buyer behavior • Goods connected by supply chains • Ideas connected semantically
- 3. Network Structures • Hubs • Densely-connected regions • Tight-knit friends groups, cities with many international flights, watering holes where many animals congregate • Bridges • Connections between regions • Individuals that span many social groups, manufacturers that provide common parts to many industries, common food sources for many types of animals
- 4. • More computationally feasible for many data science problems than traditional approaches • Spatial regression vs. network science for change point detection • Time series methods vs. network science methods • Nice visual representation of data and algorithms • Many deep connections to mathematics • Topology • Geometry • Dynamic systems
- 5. Case Studies
- 6. Case 1: Epidemic Spread Problem Predict and stop epidemic starting on a friendship network
- 7. Case 1: Epidemic Spread Data Collected dataset of friendships Static relationships (no changes over time) Represents medical school friendships and veterans’ group friendships Theoretical disease spread to predict severity within network and strategies to prevent disease spread
- 8. Case 1: Epidemic Spread Methods • SIR model • System of differential equations • Adapted for connectivity of network • Forman-Ricci curvature • Geometric measurement of centrality • Removal of highest-ranked vertex (highest risk for epidemic spread)
- 9. Case 2: Stock Market Prediction Problem • American stock exchange crash forecasting • Change-point problem in time series analytics • Caveats of non-stationary data • Difficult to model time series data at scale
- 10. Case 2: Stock Market Prediction Data • Apple, Alphabet, Nvidia, and Microsoft • 8/19/2004-4/1/2020 • Periodic trends of constant growth, crashes, and accelerated growth that sometimes overlap across stocks (and sometimes doesn’t!)
- 11. Case 2: Stock Market Prediction Methods • Overlapping time windows • Thresholded correlation networks • Changes in Forman-Ricci curvature, betweenness centrality, PageRank centrality, and degree centrality to assess risk
- 12. • Predicting millet price in markets across Burkina Faso • Impacted by supply chain and global trends (COVID 19, war in Ukraine…) • Spatiotemporal aspects • Computational cost of spatial regression models • Non-stationarity
- 13. • Quarterly millet prices • Time period of 2015 (Quarter 2) to 2022 (Quarter 2) • 45 administrative provinces (averaged market prices)
- 14. Case 3: Food Pricing Methods • Overlapping time windows • Local Moran statistic thresholding to create network • PageRank and Forman-Ricci curvature centrality to assess risk
- 15. • Benefits of network science approaches • Computational feasibility • Easy visualizations • Interpretable results • Future directions • Spatiotemporal data applications • Temporal data applications • Scaling of problems
- 16. Software Packages • Python • igraph • networkX • R • Igraph • Books • The Shape of Data