标题： GraphProp：使用图属性训练图基础模型 显示英文标题

标题： GraphProp: Training the Graph Foundation Models using Graph Properties

摘要： 这项工作专注于训练具有强大泛化能力的图基础模型（GFMs），这些模型在图级任务如图分类中表现优异。 有效的GFM训练需要捕捉不同领域间一致的信息。 我们发现，与节点特征和图标签相比，图结构提供了更多一致的跨领域信息。 然而，传统的GFMs主要关注将不同领域的节点特征转移到统一的表示空间，但往往缺乏结构上的跨领域泛化能力。 为了解决这个问题，我们引入了GraphProp，它强调结构泛化。 GraphProp的训练过程包括两个主要阶段。 首先，我们通过预测图不变量来训练一个结构化的GFM。 由于图不变量是仅依赖于图的抽象结构而非特定标签或绘制方式的图的属性，这种结构化的GFM具有强大的捕捉抽象结构信息的能力，并能提供跨多样化领域的可区分图表示。 在第二阶段，我们使用结构化GFM提供的表示作为位置编码来训练一个全面的GFM。 这一阶段利用领域特定的节点属性和图标签进一步提升跨领域节点特征的泛化能力。 我们的实验表明，GraphProp在监督学习和少样本学习中显著优于竞争对手，尤其是在处理没有节点属性的图时。 显示英文摘要

摘要： This work focuses on training graph foundation models (GFMs) that have strong generalization ability in graph-level tasks such as graph classification. Effective GFM training requires capturing information consistent across different domains. We discover that graph structures provide more consistent cross-domain information compared to node features and graph labels. However, traditional GFMs primarily focus on transferring node features from various domains into a unified representation space but often lack structural cross-domain generalization. To address this, we introduce GraphProp, which emphasizes structural generalization. The training process of GraphProp consists of two main phases. First, we train a structural GFM by predicting graph invariants. Since graph invariants are properties of graphs that depend only on the abstract structure, not on particular labellings or drawings of the graph, this structural GFM has a strong ability to capture the abstract structural information and provide discriminative graph representations comparable across diverse domains. In the second phase, we use the representations given by the structural GFM as positional encodings to train a comprehensive GFM. This phase utilizes domain-specific node attributes and graph labels to further improve cross-domain node feature generalization. Our experiments demonstrate that GraphProp significantly outperforms the competitors in supervised learning and few-shot learning, especially in handling graphs without node attributes.