Finding Successful Startups by Using Information Flows Among Investors in Higher Order Network of Investments

Finding potential successful startups is always a key issue for industrial innovation and economic development, yet it poses a significant challenge due to the complexity of investments and low success rates. Compared with existing models on knowledge correlations among pairwise startups in a first-order perspective, potential dependencies among sequential investment behaviors reveal knowledge correlations among multiple startups, which requires modeling from a higher order perspective. In this article, a novel higher order network (HON) framework, generated by dependencies among investment behaviors with timestamps, is proposed to identify the pattern of knowledge flows among startups, which has been approved higher accuracy in predicting investment behaviors. Moreover, we introduce a HON-based centrality indicator to measure the importance of startups. Experiments compared with baseline models have shown that the startups identified by proposed indicator are more influential in knowledge propagation and are closer to success. An empirical study conducted by Crunchbase database further reveals that internet-based startups occupy a significant position in investment landscapes, with those associated with finance and commerce not only attracting considerable investments but also facilitating greater success for related startups.