Tribe Style Graph

Tribe-style graphs represent complex relationships between entities, such as companies and their shareholders, or individuals within social networks, as interconnected "tribes." Research focuses on leveraging these graph structures, often employing hierarchical graph neural networks, to analyze and predict outcomes within these systems, such as financial risk or election results. Current work investigates the effectiveness of various graph-based metrics, like the influence gap, in predicting outcomes, particularly in scenarios with multiple parties or communities, and explores how these models can be improved by incorporating additional information, such as initial vote counts or financial news. This approach offers a powerful framework for analyzing complex systems and has implications for diverse fields, including finance and social sciences.