Bring Me a Good One: Seeking High-potential Startups using Heterogeneous Venture Information Networks

Abstract

The rapid acceleration of technology and the evolving global economy have led to a significant surge in high-potential startups, presenting immense opportunities for venture capital firms and investors to support and benefit from these innovative ventures. However, identifying startups with the highest likelihood of success remains a complex task, necessitating the examination of various information sources, including firm demographics, management team composition, and financial performance. The effectiveness of existing methodologies, such as feature-based and network-topological approaches, is limited for predicting high-potential startups. In response, we propose a novel Venture Graph Neural Network (VenGNN) model, leveraging Heterogeneous Information Networks (HIN) and Graph Neural Networks (GNN) techniques to address the prediction problem. Our experimental analysis reveals that VenGNN outperforms state-of-the-art models by 15-20% across a wide range of performance metrics.