Autoregressive Graph

Autoregressive graph generation focuses on creating new graph structures by sequentially predicting nodes and edges, mimicking how language models generate text. Current research emphasizes overcoming the inherent ordering challenges in graphs, exploring model architectures like recurrent neural networks and transformers, and developing novel ordering schemes such as latent sorting to improve generation accuracy. This field is significant for its applications in diverse areas like molecular design and natural language processing, where generating realistic and diverse graph structures is crucial for advancing scientific understanding and technological capabilities. Improving the efficiency and accuracy of autoregressive graph generation is a key focus, particularly in handling sparse or high-dimensional data.