## 6 Conclusions and Perspectives
The lack of adaptability to sectors other than industrial production justifies further research on databases other than Product Space to obtain results in non-standardized or service-oriented environments. As a result, Product Space may lack universality, underlining the importance of context-specific interpretations and applications. Working with Product Space means being time sensitive. Indeed, industrial production is subject to changes over time, such as market dynamics, technological advances, and economic fluctuations. Although we have worked with several historical versions of Product Space, the time dimension influences future recommendations for industrial diversification. The limitations described above will guide future plans and research prospects. This study may spark further research into more advanced and tailored graph learning techniques for Product Space clustering. Exploring variations in existing graph-based methods or developing novel algorithms can lead to more accurate and efficient clustering results, especially in scenarios where the Product Space is complex and high-dimensional. Exploring innovative algorithms and incorporating advanced methods like federated learning or edge computing could enhance the accuracy and efficiency of clustering results, especially in complex and high-dimensional Product Space scenarios. Considering the dynamic nature of industrial production and economic activities, future studies could investigate how to incorporate temporal dynamics into Product Space clustering. This may involve analyzing the evolution of product connections and studying how the clustering results change over time, enabling policymakers to make informed decisions about diversification strategies. Leveraging external data sources, such as trade data, supply chain information, or macroeconomic indicators, could enrich the Product Space analyses. Integrating such data could lead to a more comprehensive understanding of the factors that influence industrial diversification and help in identifying potential growth opportunities. Also, the study can expand its applicability by developing a framework that accommodates industries beyond the standard harmonized system nomenclature proposed by Product Space, such as service-based sectors and custom production. This could involve devising new clustering and graph learning techniques tailored to diverse industrial landscapes. Focusing on interpreting the cluster insights obtained from the Product Space clustering can provide valuable knowledge for policymakers and industry stakeholders. This will require the development of user-friendly software tools or platforms that enable industry professionals and policymakers to obtain diversification recommendations without the need for a comprehensive grasp of the underlying technology. By pursuing these research directions, the study does not just contribute to a more comprehensive understanding of industrial diversification, but also offers practical solutions and insights for decision-makers in various industries. This study offers a foundation for further research in the fields of economic diversification and industrial development. These scientific perspectives can advance the understanding of Product Space analysis and provide valuable insights for policymakers and industrial stakeholders seeking to promote economic growth and diversification. Conceptualization, K.C. ; methodology, K.C. ; software, K.C. ; validation, K.C. ; formal analysis, K.C. ; investigation, K.C. ; resources, K.C. ; data curation, K.C. ; writing--original draft preparation, K.C. ; writing--review and editing, K.C. and F.C. ; visualization, K.C. ; supervision, A.A.-K. and F.C. ; project administration, A.A.-K. and F.C. ; funding acquisition, A.A.-K. All authors have read and agreed to the published version of the manuscript.