# Predicting success in the worldwide start-up network

## TL;DR

This paper introduces a network-based method using large-scale online data to predict start-up success by analyzing professional relationships, outperforming traditional screening methods and aiding investors, entrepreneurs, and policymakers.

## Contribution

It presents a novel approach leveraging dynamic network analysis and centrality measures to forecast start-up performance, enhancing prediction accuracy over existing methods.

## Key findings

- Network centrality predicts long-term success.
- Method doubles the performance of current venture fund models.
- Network analysis offers a scalable alternative to traditional screening.

## Abstract

By drawing on large-scale online data we construct and analyze the time-varying worldwide network of professional relationships among start-ups. The nodes of this network represent companies, while the links model the flow of employees and the associated transfer of know-how across companies. We use network centrality measures to assess, at an early stage, the likelihood of the long-term positive performance of a start-up, showing that the start-up network has predictive power and provides valuable recommendations doubling the current state of the art performance of venture funds. Our network-based approach not only offers an effective alternative to the labour-intensive screening processes of venture capital firms, but can also enable entrepreneurs and policy-makers to conduct a more objective assessment of the long-term potentials of innovation ecosystems and to target interventions accordingly.