Based Network

Based networks leverage graph structures to model relationships between entities, aiming to improve the efficiency and robustness of various applications. Current research focuses on enhancing model resilience to noisy or incomplete data through techniques like negative sampling and novel loss functions, as well as developing more efficient architectures such as MLP-based approaches that avoid computationally expensive message-passing. These advancements are impacting fields like anomaly detection, path planning, and federated learning by enabling more accurate and privacy-preserving solutions, particularly in scenarios with incomplete or unreliable data.