## 6 Conclusions and Perspectives  
This study assesses the effectiveness of graph learning techniques for filtering recommendations from macroeconomic graphs such as the Product Space. The proposed graph learning method is applied to the Product Space dataset, which reflects the similarity in industrial expertise among product classes within the HS nomenclature, irrespective of geographical boundaries. The resulting clusters filter the many recommendations offered by the Product Space. The recommendations propose diversifications of production for manufacturers to mitigate the risk of shortages while promoting local innovation and green \begin{table}
\begin{tabular}{c c c c} \hline  & **Louvain** & **I-Louvain** & **GraphSAGE** \\ \hline Rand Index & 0.674 \(\pm\) 0.003 & 0.675 \(\pm\) 0.001 & 0.689 \(\pm\) 0.001 \\ Mutual Info Score & 0.378 \(\pm\) 0.014 & 0.408 \(\pm\) 0.001 & 0.498 \(\pm\) 0.003 \\ V-measure & 0.167 \(\pm\) 0.006 & 0.176 \(\pm\) 0.001 & 0.207 \(\pm\) 0.002 \\ \hline \end{tabular}
\end{table} 
Table 2: Evaluation of Product Space according to metrics. Assessments were calculated five times, and the results are the mean and standard deviation for the five runs. \begin{table}
\begin{tabular}{c c c c} \hline  & **Louvain** & **I-Louvain** & **GraphSAGE** \\ \hline Rand Index & 0.839 \(\pm\) 0.002 & 0.801 \(\pm\) 0.001 & 0.848\(\pm\) 0.007 \\ Mutual Info Score & 0.911 \(\pm\) 0.027 & 0.902 \(\pm\) 0.001 & 0.924 \(\pm\) 0.030 \\ V-measure & 0.448 \(\pm\) 0.005 & 0.395 \(\pm\) 0.001 & 0.498 \(\pm\) 0.018 \\ \hline \end{tabular}
\end{table} 
Table 3: Evaluation of Cora according to metrics. Assessments were calculated five times, and the results are the mean and standard deviation for the five runs. economic growth. The use of deep graph learning methods, in particular, GraphSAGE, allows updating node embedding for better representation in their graph. This approach demonstrates improved performance in community detection across multiple datasets, compared with alternative methods that are based on modularity. Additionally, the recommendations offered by GraphSAGE filtering are more relevant when tested in the field with manufacturers. While GraphSAGE performs better for node clustering in graphs, it is essential to consider these scientific limitations when applying them in specific contexts. GraphSAGE can be computationally and memory-intensive, especially for large graphs. Handling massive graphs requires significant computational power, which may limit its usage in certain applications. Like many neural-network-based machine learning techniques, interpreting GraphSAGE results can be challenging. Understanding how clusters are formed and why certain nodes are grouped together is complex. Also, the random neighbor sampling approach can lead to sampling bias and in some cases not adequately explore the various neighborhoods of the graph. GraphSAGE might exhibit suboptimal performance on graphs characterized by a high degree of homophily, wherein nodes sharing similar attributes are likely to be connected. To make diversification recommendations based on the Product Space, we are limited by this data. Indeed, the high degree of homophily in the Product Space graph is a limitation. The consequence is that products with similar text descriptions tend to be already connected. So, for products with a low number of neighbors, recommendation filtering will not add value in these cases. One of the shortcomings of our work concerns the generalization of our results to other industry-specific contexts. By working with Product Space, the application of our methods limits us to other contexts such as the service sector and made-to-measure production, as we are working with products from the harmonized system. This specificity limits the contribution of our results and highlights the need for caution when extrapolating them to different fields.