LAUSR

Startups form an information network that reflects their growth trajectories through information flow channels established by shared investors. However, traditional static network metrics overlook temporal dynamics and rely on single indicators to assess startups’ roles in predicting future success, failing to comprehensively capture topological variations and structural diversity. To address these limitations, we construct a temporal information network using 14547 investment records from 1013 global blockchain startups between 2004 and 2020, sourced from Crunchbase. We propose two dynamic methods to characterize the information flow: temporal random walk (sTRW) for modeling information flow trajectories and temporal betweenness centrality (tTBET) for identifying key information hubs. These methods enhance walk coverage while ensuring random stability, allowing for more effective identification of influential startups. By integrating sTRW and tTBET, we develop a comprehensive metric to evaluate a startup’s influence within the network. In experiments assessing startups’ potential for future success — where successful startups are defined as those that have undergone M&A or IPO — incorporating this metric improves accuracy, recall, and F1 score by 0.035, 0.035, and 0.042, respectively. Our findings indicate that information flow from key startups to others diminishes as the network distance increases. Additionally, successful startups generally exhibit higher information inflows than outflows, suggesting that actively seeking investment-related information contributes to startup growth. Our research provides valuable insights for formulating startup development strategies and offers practical guidance for market regulators.