Description: Link prediction in multimodal networks is a task focused on identifying and predicting missing links between nodes in a network that contains multiple types of entities and relationships. These multimodal networks are complex as they integrate different types of nodes, such as users, products, and services, each with its own characteristics and relationships. Link prediction is crucial for understanding the structure and dynamics of these networks, allowing researchers and professionals to uncover hidden patterns and significant relationships. This process relies on machine learning techniques and graph analysis, where algorithms are used to model interactions between nodes and predict possible connections that do not yet exist. The ability to predict missing links not only enhances the understanding of the network but can also optimize recommendations, improve information retrieval, and facilitate decision-making in various applications, from social networks to recommendation systems and data analysis. In summary, link prediction in multimodal networks is a powerful tool for unraveling the complexity of interactions in systems involving multiple types of data and relationships.
