Graph Generative Pre-trained Transformer (G2PT): An Auto-Regressive Model Designed to Learn Graph Structures through Next-Token Prediction
Graph generation is an important task across various fields, including molecular design and social network analysis, due to its ability to model complex relationships and structured data. Despite recent advancements, many graph generative models still rely heavily on adjacency matrix representations. While effective, these methods can be computationally demanding and often lack flexibility. This can […]
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Summary
The article discusses the Graph Generative Pre-trained Transformer (G2PT), an auto-regressive model designed to learn graph structures through next-token prediction. Graph generation is crucial in various fields like molecular design and social network analysis for modeling complex relationships and structured data. The G2PT aims to improve upon existing graph generative models that heavily rely on adjacency matrix representations, which can be computationally demanding and lack flexibility.
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