Description: Multimodal network-based models use network structures to represent and analyze relationships between different modalities, such as text, images, audio, and video. These models can integrate and process information from various sources, allowing them to capture the complexity of real-world data. By utilizing nodes and connections, interactions and dependencies between different modalities can be established, facilitating a deeper and more contextualized analysis. The main characteristics of these models include their ability to learn joint representations, their flexibility to adapt to different types of data, and their potential to improve accuracy in classification and prediction tasks. The relevance of multimodal network-based models lies in their application in the broader field of artificial intelligence, where the goal is to develop systems that can understand and generate content in a more human-like and natural manner. By combining different types of data, these models not only enrich the available information but also allow for a better understanding of the interactions between modalities, resulting in superior performance in complex tasks.
