让图神经网络在分布外链接预测中更稳定，无需专家经验。
Subgraph Generation for Generalizing on Out-of-Distribution Links
- 用结构条件生成和对抗协同训练提升图数据分布对齐
- 在真实与合成分布外场景下显著提升链接预测性能
- 无需领域知识，可通用适配多种分布外场景
图神经网络（GNN）在链接预测任务中表现优异，但通常依赖于所有样本来自同一分布。图生成模型（GGM）虽能生成新图，但应用仍局限于特定领域。为此，我们提出FLEX框架，融合两种机制：(1) 结构条件图生成，(2) 自编码器与GNN间的对抗协同训练。FLEX通过增强样本分布的结构对齐，显著提升分布外（OOD）场景下的链接预测性能。值得注意的是，FLEX无需专家知识即可适应不同OOD场景。我们在合成与真实世界分布外设置中进行了大量实验，验证了其性能提升能力，并进一步分析了图数据增强对链接结构的影响。源代码已公开：https://github.com/revolins/FlexOOD。
原文摘要 · Abstract (English)
Graphs Neural Networks (GNNs) demonstrate high-performance on the link prediction (LP) task. However, these models often rely on all dataset samples being drawn from the same distribution. In addition, graph generative models (GGMs) show a pronounced ability to generate novel output graphs. Despite this, GGM applications remain largely limited to domain-specific tasks. To bridge this gap, we propose FLEX as a GGM framework which leverages two mechanism: (1) structurally-conditioned graph generation, and (2) adversarial co-training between an auto-encoder and GNN. As such, FLEX ensures structural-alignment between sample distributions to enhance link-prediction performance in out-of-distribution (OOD) scenarios. Notably, FLEX does not require expert knowledge to function in different OOD scenarios. Numerous experiments are conducted in synthetic and real-world OOD settings to demonstrate FLEX's performance-enhancing ability, with further analysis for understanding the effects of graph data augmentation on link structures. The source code is available here: https://github.com/revolins/FlexOOD.
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