title: Graph Generative Pre-trained Transformer
summary: 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.
paper_authors._id: 677b7e4ef3f3f282a1051101
paper_authors.name: Xiaohui Chen
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-01-06T06:55:12.787Z
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paper_authors.avatarUrl: /avatars/e630e72a9d80a4d73bbf979133a0e07a.svg
paper_authors.fullname: Xiaohui Chen
paper_authors.type: user
paper_authors.user: xchen16
paper_authors._id: 677b7e4ef3f3f282a1051102
paper_authors.name: Yinkai Wang
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/4392649682d87e99f396a2bff8a90b6e.svg
paper_authors.fullname: Yinkai Wang
paper_authors.type: user
paper_authors.user: Spony
paper_authors._id: 677b7e4ef3f3f282a1051103
paper_authors.name: Jiaxing He
paper_authors.status: admin_assigned
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paper_authors._id: 666a81d55aa2df95fad831b6
paper_authors.avatarUrl: /avatars/de72f369f3841c8c4071520755ad267a.svg
paper_authors.fullname: He Jiaxing
paper_authors.type: user
paper_authors.user: RArchered
paper_authors._id: 677b7e4ef3f3f282a1051104
paper_authors.name: Yuanqi Du
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-06T08:31:13.712Z
paper_authors._id: 632513c778ba0c1d795513c9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1663374211032-noauth.jpeg
paper_authors.fullname: Yuanqi Du
paper_authors.type: user
paper_authors.user: y6q9
paper_authors._id: 677b7e4ef3f3f282a1051105
paper_authors.name: Soha Hassoun
paper_authors._id: 677b7e4ef3f3f282a1051106
paper_authors.name: Xiaolin Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-06T08:31:24.763Z
paper_authors._id: 670cc73815f57d098521cbad
paper_authors.avatarUrl: /avatars/2aeac179f215a344635e2aa0a7a64f7c.svg
paper_authors.fullname: XIaolin Xu
paper_authors.type: user
paper_authors.user: XiaolinXu
paper_authors._id: 677b7e4ef3f3f282a1051107
paper_authors.name: Li-Ping Liu
paper_title: Graph Generative Pre-trained Transformer
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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