Lucas Durand - Building an Interactive Network Graph to Understand Communities | PyData Global 2023

Network graphs can be used to visualize and analyze relationships between people in organizations, showing reporting structures, team connections, and shared attributes

NetworkX is the primary Python library for creating and manipulating network graphs, while visualization can be done with libraries like Plotly, Pyvis, and Dash

Faker library is useful for generating synthetic but realistic-looking people data, including names, locations, job titles while maintaining privacy

Graph nodes can represent people or attributes (like programming languages, locations), while edges show relationships/connections between nodes

Force-directed layouts help position nodes by treating edges like springs, pushing unconnected nodes apart and pulling connected ones together

Interactive visualization features like hovering, filtering, and coloring by attributes help explore complex relationships in the graph

For large graphs (thousands of nodes), consider using distributed computing solutions like Spark or specialized graph databases like Neo4j

Combining multiple attributes (location, team, skills) helps identify clusters and find similar people beyond just reporting structure

Graph analysis can help find influential nodes, communities, and shortest paths between people in the network

Building interactive web applications with Dash allows non-technical users to explore and interact with network graphsEnd File# KovachYuriy/BlackFriday-GPTs-Prompts