Conditional Graph Generation

Conditional graph generation focuses on creating graphs that satisfy specific constraints or conditions, aiming to produce structured data tailored to particular needs. Recent research emphasizes developing models that address challenges like efficient generation of diverse graph structures, handling imbalanced datasets, and incorporating interpretability and controllability. Prominent approaches include diffusion models, autoregressive models, and graph neural networks, often combined with techniques like adaptive sparsity and learning-based ordering schemes to improve generation quality and efficiency. These advancements have significant implications for various fields, including drug discovery, anomaly detection in IoT networks, and natural language processing applications like empathetic response generation.