Graphs offer a powerful tool for uncovering relationships and hidden patterns between entities. This presentation provides an introduction to graph theory, and demonstrates how graphs are applied across various business domains - from social network analysis and recommendation systems to fraud detection and supply chain optimization. Through an overview of core concepts and algorithmic approaches, the goal is to show how graphs can reveal underlying structures in complex systems.

As a real-world case study, the presentation focuses on Eurovision voting - an area long suspected of regional bias and neighbourly favoritism. Using graph-based analysis and community detection techniques, we explore voting patterns between countries to better understand how geography, history, and politics influence the distribution of points. Through visualizations and data-driven insights, the talk demonstrates how graph theory uncovers hidden alliances and behavioral trends within the seemingly light-hearted spectacle of a music contest.

Devoxx

A conference talk applying graph theory and community detection algorithms to Eurovision Song Contest voting data. Using Python's NetworkX library and the Louvain algorithm with Monte Carlo consensus clustering, the speaker analyzes 2008–2024 voting data to confirm neighborly favoritism patterns. Results show geographic voting blocs (ex-Yugoslav countries, Nordic bloc, Iberian countries) emerge naturally without the algorithm knowing geography. Additional findings: Italy consistently outperforms other 'Big Five' countries, jury votes show less geographic bias than public votes, and Sweden has unusually high betweenness centrality in jury voting. The talk also covers graph theory fundamentals, business use cases for graphs, and suggests future extensions like overlapping community detection and demographic correlation analysis.

Friendly Borders: Graph algorithms reveal Eurovision voting patterns by Domagoj Marić