How to Turn Financial Data Into a Readable Network | Lecture 5

This lecture moves from theory to implementation by showing how financial networks are represented, traversed, and visualized in code. You will learn when to use edge lists versus adjacency matrices, how NetworkX turns tabular data into graph objects, how layout algorithms change interpretation, and how styling decisions reveal or conceal structure. The lecture also covers decluttering techniques such as filtering, edge bundling, labeling discipline, and interactive graph tools like Plotly and PyVis, making it a practical guide to readable and useful financial network visualization.

Timestamps
00:00 Introduction
00:55 Different ways to store a graph
02:05 Different type of Graphs
05:25 Graph Traversal
06:25 Graph Visualization
11:45 Taming the complexity - Hairball
16:15 Summary

Hashtags: #NetworkVisualization #NetworkX #FinancialNetworks #PythonForFinance #GraphTheory

Видео How to Turn Financial Data Into a Readable Network | Lecture 5 канала Signal x Capital