PageRank Betweenness and More in Buyer-Seller Networks From Market Data to Bipartite Graph - Concept

Most transaction datasets contain hidden structure that flat tables do not reveal: who depends on whom, who brokers flows, where concentration risk lives, and how buyer types cluster. In this video lecture, I show how to turn transaction records into a buyer-seller economic networks and how to interpret the main network metrics in an economics and market structure way.
This video presents network data and metrics applied to buyer-seller economic networks. It details concepts like PageRank, baseline metrics, and dependency risk concentration using HHI, providing insights into network analysis. We also discuss how to interpret PageRank in these networks and the informative nature of betweenness centrality, crucial for understanding complex systems and economic modeling.
Network metrics are a practical toolkit for economic microdata where relationships matter as much as size. In this Part 1 lecture, I explain the core logic behind centrality, brokerage, concentration, similarity, and community structure in buyer seller networks, with the goal of setting up a clean Python implementation in Part 2.

What you will learn in this video (theory first, code next):
How to model economic relations as a weighted network W and a transition matrix P
Why measurement choices change the economics (weights, direction, window, trimming, missing links)
Strength (weighted degree) as the baseline benchmark
PageRank and walk based influence as recursive prominence in buyer seller networks
Intro to BiRank intuition for two group propagation in bipartite graphs
Betweenness as brokerage, choke points, and bottleneck risk
Dependency concentration using HHI on supplier shares (not market definition HHI)
Buyer buyer similarity networks using cosine similarity on procurement baskets
Community detection and modularity as descriptive segmentation, not identification

Part 2: Python implementation of these metrics plus visualizations on buyer seller networks, including bipartite layouts and projected buyer buyer or seller seller networks.

If you are intersted in network analysis and work with trade, supply chain, credit, payment, procurement, or platform transaction data, this is the conceptual map that keeps your metrics interpretable and defensible.

0:00 Intro: why networks in economics
2:48 The video outline
6:00 Initial Concepts: Adjacency matrix, Measurement choices that change interpretation, Degree or Strength
13:58 PageRank and recursive influence
22:56 BiRank - brief intro
25:05 Betweenness and bottleneck logic
31:54 HHI as dependency concentration
34:37 Cosine similarity buyer types and peer sets
38:37 Community detection and modularity cautions
43:37 Part 2 preview: Python metrics and visualization

#NetworkAnalysis #Economics #PageRank #BetweennessCentrality #SupplyChain #DataScience #GraphTheory