A Temporal Network Approach to Modeling Quantitative Success in Venture Capital Ecosystems

Abstract

This thesis investigates how temporal network structures can predict financial success in early-stage startups. Using investment data from Pitchbook, it constructs a dynamic graph of the North American Venture Capital (NAVC) ecosystem, capturing evolving relationships between investors and startups over time. From this network, node-level features such as temporal centrality and community embedding are computed to represent each startup’s structural identity. These features are used as inputs to train an Extreme Gradient Boosting (XG- Boost) supervised ML model, to predict a binary classification target of successful exits (IPO or acquisition) or failure within a fixed time window. Results show that models incorporating temporal network features consistently outperform baselines and results from similar problems, particularly on Precision@K metrics, which are practically relevant to VC decision-making. The findings demonstrate that interpretable, time-aware network metrics can meaningfully enhance startup evaluation frameworks. This work contributes to the intersection of finance, network science, and predictive modeling, offering new tools for data-driven early-stage investment.