Relational Graph

Relational graphs represent data as nodes and edges, where edges encode relationships between nodes, enabling the modeling of complex interactions within diverse datasets. Current research focuses on developing and improving graph neural network (GNN) architectures, such as relational graph convolutional networks (RGCNs), to effectively learn from and reason over these relational structures, with a particular emphasis on enhancing explainability and scalability for large graphs. This work has significant implications for various fields, including knowledge graph reasoning, sentiment analysis, molecule design, and image generation, by enabling more accurate and interpretable models for complex data.