Graph Generative Pre-trained Transformer
Graph generation is a critical task in numerous domains, including molecular
design and social network analysis, due to its ability to model complex
relationships and structured data. While most modern graph generative models
utilize adjacency matrix representations, this work revisits an alternative
approach that represents graphs as sequences of node set and edge set. We
advocate for this approach due to its efficient encoding of graphs and propose
a novel representation. Based on this representation, we introduce the Graph
Generative Pre-trained Transformer (G2PT), an auto-regressive model that learns
graph structures via next-token prediction. To further exploit G2PT's
capabilities as a general-purpose foundation model, we explore fine-tuning
strategies for two downstream applications: goal-oriented generation and graph
property prediction. We conduct extensive experiments across multiple datasets.
Results indicate that G2PT achieves superior generative performance on both
generic graph and molecule datasets. Furthermore, G2PT exhibits strong
adaptability and versatility in downstream tasks from molecular design to
property prediction.
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Xiaohui Chen
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2025-01-06T06:55:12.787Z
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Xiaohui Chen
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Yinkai Wang
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2025-01-06T08:30:56.674Z
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Yinkai Wang
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Spony
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Jiaxing He
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2025-01-06T08:31:07.412Z
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He Jiaxing
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RArchered
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Yuanqi Du
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2025-01-06T08:31:13.712Z
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Yuanqi Du
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y6q9
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Soha Hassoun
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Xiaolin Xu
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2025-01-06T08:31:24.763Z
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XIaolin Xu
user
XiaolinXu
677b7e4ef3f3f282a1051107
Li-Ping Liu
Graph Generative Pre-trained Transformer
Graph generation is a critical task in numerous domains, including molecular
design and social network analysis, due to its ability to model complex
relationships and structured data. While most modern graph generative models
utilize adjacency matrix representations, this work revisits an alternative
approach that represents graphs as sequences of node set and edge set. We
advocate for this approach due to its efficient encoding of graphs and propose
a novel representation. Based on this representation, we introduce the Graph
Generative Pre-trained Transformer (G2PT), an auto-regressive model that learns
graph structures via next-token prediction. To further exploit G2PT's
capabilities as a general-purpose foundation model, we explore fine-tuning
strategies for two downstream applications: goal-oriented generation and graph
property prediction. We conduct extensive experiments across multiple datasets.
Results indicate that G2PT achieves superior generative performance on both
generic graph and molecule datasets. Furthermore, G2PT exhibits strong
adaptability and versatility in downstream tasks from molecular design to
property prediction.
A Graph Generative Pre-trained Transformer (G2PT) using node and edge sequences outperforms existing graph generative models and demonstrates versatility in tasks like molecular design and property prediction.
graph generative models adjacency matrix node set edge set Graph Generative Pre-trained Transformer G2PT auto-regressive model next-token prediction goal-oriented generation graph property prediction