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Colleen Farrelly, Franck Kalala Mutombo, & Yae U. Gaba- Hands-On Network Science| PyData Global 2023
Explore the power of network science for solving complex data problems, from data visualization and connectivity measurement to predicting stock market volatility and identifying change points in time series data.
- Network science is a powerful tool for solving complex data problems, especially when data is spatiotemporal, and assumptions of stationarity are not met.
- Forman-Ritchie curvature is a geometric metric that can be used to visualize and measure connectivity in networks.
- Correlation is not always the best metric for networks, and thresholds can be set to create networks that are more interpretable.
- Epidemics can be slowed or stopped by removing key nodes or bridges in a network.
- Network science can be used to predict stock market volatility and identify change points in time series data.
- Spatial and temporal regression models can be used to analyze data, but require a lot of data and computational resources.
- Network science is connected to various fields, including mathematics, topology, geometry, and dynamic systems theory.
- Python and R are popular programming languages for network science, with packages available for iGraph, NetworkX, and others.
- Handson Network Science is a book that introduces network science concepts and applications.
- The Shape of Data is a book that provides an introduction to network science and its applications.
- Bicycle-like structures in networks are called bridges, and they can be key to disease spread or information transmission.