Heterogeneous Network

Heterogeneous networks, encompassing systems with diverse node types and connections, are a focus of intense research aiming to understand and optimize their complex behavior. Current efforts concentrate on developing novel algorithms and model architectures, such as graph neural networks, reinforcement learning, and federated learning, to address challenges in data heterogeneity, resource allocation, and efficient communication across diverse network structures. This research is significant for advancing diverse applications, including improved object detection in autonomous vehicles, personalized medicine through LncRNA-disease association prediction, and enhanced efficiency in wireless communication networks.