用序列化方式生成图，性能优于传统方法
Graph Generative Pre-trained Transformer
- 将图表示为节点与边的序列，实现高效编码
- 在分子和通用图数据集上生成效果更优
- 适合分子设计与属性预测等下游任务
图生成在分子设计和社交网络分析等领域至关重要，因其能建模复杂关系与结构化数据。现有模型多采用邻接矩阵表示图，本文提出一种新思路：将图表示为节点集与边集的序列。该表示法具有高效编码优势，并在此基础上构建了图生成预训练变压器（G2PT），一个基于自回归机制、通过预测下一个词元来学习图结构的模型。为进一步挖掘G2PT作为通用基础模型的能力，我们探索了其在目标导向生成和图属性预测两类下游任务中的微调策略。在多个数据集上的实验表明，G2PT在通用图和分子数据集上均展现出卓越的生成性能，且在从分子设计到属性预测的任务中表现出强适应性与通用性。代码已公开于 https://github.com/tufts-ml/G2PT。
原文摘要 · Abstract (English)
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. Code available at https://github.com/tufts-ml/G2PT,
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