Understanding User Interactions and Community Formation on Data Competition Platform

As increasing volumes of data have become available globally, there are growing expectations for the creation of value through the exchange of data across diverse fields and the enhancement of the value of existing services. Consequently, there is an increasing demand for markets and platforms to facilitate data trading and exchanges. However, unlike other well-established business ecosystems such as existing financial markets and service ecosystems, the overall structure and characteristics of the data exchange ecosystem remain largely unexplored. This study focuses on users who handle data, and aims to elucidate their behaviors and identify influential users on the platforms, thereby deepening our understanding of the data ecosystem. We conducted network analysis, community extraction, and time-series analysis of the behavior of Kaggle users. Our results indicate that the network of Kaggle users exhibits a scale-free structure similar to typical social networks and web links. This suggests that information dissemination among Kaggle users is likely to occur through a small number of hubs. Furthermore, we performed community extraction, analyzed the behavior of users in each community, and discovered that each community possessed distinct user behavior characteristics. Subsequently, we analyzed the number of influential users with numerous followers or high degree centrality and examined the time-series changes in the number of actions taken by these users. This analysis enabled us to identify the behaviors and formation patterns of those who played a central role in the data platform. The approach and findings of our study provide important considerations for stakeholders and potential participants in all the markets and platforms that handle data.