GNN Model

Graph Neural Networks (GNNs) are a class of machine learning models designed to analyze and learn from graph-structured data, aiming to extract meaningful representations of nodes and edges for various downstream tasks like node classification and link prediction. Current research emphasizes improving GNN expressiveness through novel architectures and algorithms, such as those based on higher-order relationships and incorporating techniques like message passing and attention mechanisms to address challenges like over-smoothing and heterophily. The advancements in GNNs are significant because they enable efficient processing of complex relational data, impacting diverse fields including social network analysis, drug discovery, and cybersecurity through improved accuracy and interpretability.