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Lucas Durand - Building an Interactive Network Graph to Understand Communities | PyData Global 2023
Discover how to build an interactive network graph to understand communities using NetworkX, Dash, and GAN-based models, and explore techniques for data manipulation, visualization, and synthetic data generation.
- Building an interactive network graph to understand communities requires a combination of data manipulation and visualization techniques.
- NetworkX is a powerful library for creating, manipulating, and analyzing complex networks.
- Fake libraries like Faker can be used to generate synthetic data for testing and prototyping purposes.
- Dash is a Python framework for creating interactive, web-based applications, including network graphs.
- Graph attributes can be used to color and size nodes based on their properties.
- NetworkX allows for filtering and sampling nodes and edges based on specific criteria.
- GAN-based models can be used to generate synthetic data that mimics the properties of real-world data.
- It is important to consider data privacy and security when working with sensitive data.
- Synthetic data can be used to test and evaluate models without compromising real-world data.
- NetworkX has built-in visualization functions for visualizing network data.
- Interactive network graphs can provide valuable insights into community structures and relationships.
- Libraries like Plotly and Bokeh can be used to create interactive, web-based visualizations.
- NetworkX can be used to create complex, dynamic visualizations of network data.
- Synthetic data can be used to test and evaluate models for detecting communities and clusters in data.