Development of a method framework to predict network structure dynamics in digital platforms: Empirical experiments based on API networks

Abstract

Digital ecosystems reinforce the commercial achievements of digital innovations, providing organizations with platforms to implement digital products by sharing, co-developing, and using application programming interfaces (APIs) as digital resources. The use of APIs in digital ecosystems formulates dynamic API networks that evolve with the emergence of APIs and their updates. API network dynamics are associated with disruptive technology, heterogeneous networks, product and service innovation, and entrepreneurial success. However, methods for measuring and predicting API network dynamics have not been developed. We developed a framework for measuring and predicting the API network dynamics generated by APIs. To develop the abovementioned framework, we invented three network embeddings that could represent and measure API network dynamics and a prediction model based on a deep learning approach that could forecast API network dynamics. We conducted multiple experiments to assess the performance and usability of our method framework, and the results consistently demonstrate that our developed approach surpasses existing methods.