Predicting success in the worldwide start-up network

Abstract

By drawing on large-scale online data we are able to 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 economic performance of a start-up. We find that the start-up network has predictive power and that by using network centrality we can provide valuable recommendations, sometimes doubling the current state of the art performance of venture capital funds. Our network-based approach supports the theory that the position of a start-up within its ecosystem is relevant for its future success, while at the same time it offers an effective complement to the labour-intensive screening processes of venture capital firms. Our results can also enable policy-makers and entrepreneurs to conduct a more objective assessment of the long-term potentials of innovation ecosystems, and to target their interventions accordingly.

Figure 1

The time-varying network of professional relationships among start-ups. (A) Countries that, over time, joined the largest connected component (LCC) of the worldwide start-up (WWS) network are highlighted in blue. (B) Evolution over time of the number of firms and links in the WWS network. (C) Evolution over time of the fraction of nodes in the LCC. (D) Evolution over time of the closeness centrality rank of five popular firms. (E) Airbnb’s ego-centered network (Icon faces are by https://icon-library.net/icon/human-face-icon-2.html/CC0 Public Domain Licence).

Figure 2

Closeness-based ranking of open-deals and predicting long-term success. (A) The performance of our recommendation method in predicting companies’ success on a monthly basis compared to the expected performance of a null model (random ordering of companies). The top panel reports the probability ($p$-value) of obtaining, by chance, a success rate larger than the one observed in the corresponding month. The gray-shaded region indicates the time periods where the prediction is statistically significant (p-value < 0.05). (B) The overall performance of our method over the entire period of observation based on the Top 20, 50 and 100 firms with the highest closeness centrality. The black error bars indicate the expected success rates and standard deviations in the case of random ordering of companies. Interestingly, results of this null model are comparable to the expected success of those venture capital funds whose portfolio focus on early-stage companies similar to those considered in our open deal list (see Section S4.2 for details), and are about twice as low as our results based on network centrality.