Joint Graph

Joint graph analysis focuses on integrating information from multiple related graphs or networks to achieve improved performance in various tasks. Current research emphasizes developing efficient algorithms, such as those based on joint state graphs and graph convolutional networks, to handle the computational challenges posed by large-scale graphs and multiple agents. These methods find applications in diverse fields, including multimodal emotion recognition, multi-agent path planning, and brain connectome analysis, where integrating information from different modalities or networks enhances model accuracy and interpretability. The development of scalable and efficient joint graph learning techniques is a significant area of ongoing research with broad implications for various scientific and engineering domains.