GNN Architecture

Graph Neural Networks (GNNs) are a class of deep learning models designed to process graph-structured data, aiming to learn representations that capture both node features and relational information within the graph. Current research focuses on improving GNN expressiveness and efficiency through novel architectures like decoupled GNNs, higher-order GNNs, and attention-based models, as well as addressing challenges such as over-smoothing, bias mitigation, and out-of-distribution generalization. These advancements are significant for diverse applications, including anomaly detection, combinatorial optimization, and molecular modeling, where GNNs offer powerful tools for analyzing complex relational data.