Graph-structured data is ubiquitous in the world which models complex relationships between objects, enabling various Web applications. Daily influxes of unlabeled graph data on the Web offer immense potential for these applications. Graph self-supervised algorithms have achieved significant success in acquiring generic knowledge from abundant unlabeled graph data. These pre-trained models can be applied to various downstream Web applications, saving training time and improving downstream (target) performance. However, different graphs, even across seemingly similar domains, can differ significantly in terms of attribute semantics, posing difficulties, if not infeasibility, for transferring the pre-trained models to downstream tasks. Concretely speaking, for example, the additional task-specific node information in downstream tasks (specificity) is usually deliberately omitted so that the pre-trained representation (transferability) can be leveraged. The trade-off as such is termed as "transferability-specificity dilemma" in this work. To address this challenge, we introduce an innovative deployment module coined as GraphControl, motivated by ControlNet, to realize better graph domain transfer learning. Specifically, by leveraging universal structural pre-trained models and GraphControl, we align the input space across various graphs and incorporate unique characteristics of target data as conditional inputs. These conditions will be progressively integrated into the model during fine-tuning or prompt tuning through ControlNet, facilitating personalized deployment. Extensive experiments show that our method significantly enhances the adaptability of pre-trained models on target attributed datasets, achieving 1.4-3x performance gain. Furthermore, it outperforms training-from-scratch methods on target data with a comparable margin and exhibits faster convergence.

翻译：图结构数据在世界中无处不在，它通过对对象间复杂关系的建模支撑着各类Web应用。Web上每日涌入的大量无标注图数据为这些应用提供了巨大潜力。图自监督算法已从海量无标注图数据中获取通用知识方面取得显著成功。这些预训练模型可应用于各类下游Web应用，不仅节省训练时间，还能提升下游（目标）任务的性能。然而，不同图（即使来自看似相似的领域）在属性语义上可能存在显著差异，这给预训练模型向下游任务的迁移带来了困难，甚至可能无法实现。具体而言，例如下游任务中额外的任务特定节点信息（特异性）通常被刻意忽略，以便利用预训练表示（可迁移性）。这种权衡在本工作中被称为"可迁移性-特异性困境"。为应对这一挑战，受ControlNet启发，我们引入了一种创新的部署模块GraphControl，以实现更优的图域迁移学习。具体而言，通过利用通用结构预训练模型和GraphControl，我们对齐不同图之间的输入空间，并将目标数据的独特特征作为条件输入。这些条件将在微调或提示调优过程中通过ControlNet渐进式地整合到模型中，从而促进个性化部署。大量实验表明，我们的方法显著增强了预训练模型在目标属性数据集上的适应性，实现了1.4-3倍的性能提升。此外，该方法在目标数据上以同等优势超越了从头训练的方法，并展现出更快的收敛速度。