GCN Model

Graph Convolutional Networks (GCNs) are a class of neural networks designed to process graph-structured data, aiming to learn representations that capture relationships between nodes. Current research focuses on improving GCN performance and interpretability, addressing challenges like oversmoothing, handling heterophily, and enhancing robustness against adversarial attacks. This involves exploring novel architectures such as Lorentzian GCNs for hierarchical data and incorporating causal inference or cognitive models to improve accuracy and explainability in applications ranging from human behavior analysis to resource allocation in the metaverse. The resulting advancements have significant implications for various fields, including healthcare, knowledge graph reasoning, and computer vision.