## 2 Related Work
### Economics Graph Learning  
Economists are mainly interested in understanding economic phenomena using network concepts. The attention toward graph clustering in economic research has increased significantly owing to its capability to capture the intrinsic structure and interconnections present within economic networks. The clustering of economic agents, such as firms, industries, or regions, allows for a deeper understanding of their interactions and the emergence of complex economic phenomena [32]. Recently, there has been an emergence of several graph clustering methodologies, predominantly rooted in classical statistical techniques that solely rely on the topology of the graphs. Many statistical indicators are used in economics such as centrality, clustering tendency, or modularity optimization [33; 34]. Modularity optimization aims to increase the density of connections within clusters while decreasing the connections between clusters. Moreover, when nodes are economic agents, their assignments to a community allow predicting economic phenomena related to potential business ecosystems [32]. These economic studies have also shown that graph community detection provides a better cluster than classical clustering on non-relational economic data. When networks are large, community detection on economic graphs use frequently Louvain [35; 36], Stochastic Block Model [37] or Leiden algorithms [32]. All these methods obtain good clusters using only graph topography but without exploiting nodes and edges features. Linkage prediction methods are applied in economics to predict the evolution of future economic networks to guide policymakers. Authors use these methods to predict possible linkages in the future labor market [38]. However, these methods use adjacency matrix perturbations, for example, but without capitalizing on the data within the graphs [39]. The use of data within graphs (embedding) remains rare in economics. For example, [PERSON] et al. [40] used node data to reconstruct supply chain networks by performing a classification of possible connection pairs with Gradient Boosting. Additionally, [PERSON] et al. [41] used node data in a classification task, although in a different context from our work which is clustering. In Table 1, we review recent studies exploring graph clustering methods for applications in economics. Our analysis indicates that most of the selected studies concentrate solely on analyzing the graph topology, without any study focusing on the features of nodes and edges. Consequently, these studies restrict the generalizability of the proposed methods to leverage all available information. Moreover, it is noticeable that these methods do not capture high-level dependencies. For example, using attributes of nodes representing economic agents, it becomes possible to group entities that are similar in terms of economic behavior, even if they are not directly connected in the graph's topology. Hence, in our research, we propose a more versatile approach that incorporates node and edge features to capture higher-level dependencies among graph entities. In summary, our approach enables us to take fuller account of the information available, better capture high-level dependencies, improve the accuracy and interpretability of the results, and provide greater flexibility and adaptability to specific economic contexts. In addition, the potential application of advanced GNNs in economic graph analysis could explore aspects such as market dynamics, risk assessment, and supply chain optimization. This literature review shows that the key scientific novelty in our research emerges from our application of deep graph learning techniques within the realm of economics. To be more precise, our study introduces an innovative method for understanding and utilizing economic graphs, which goes beyond conventional analyses by encompassing the learning of node-specific data in addition to network structure.