Graphs play a central role in modeling complex relationships across various domains. Most graph learning methods rely heavily on neighborhood information, raising the question of how to handle cold-start nodes - nodes with no known connections within the graph. These models often overlook the cold-start nodes, making them ineffective for real-world scenarios. To tackle this, we propose G-SPARC, a novel framework addressing cold-start nodes, that leverages generalizable spectral embedding. This framework enables extension to state-of-the-art methods making them suitable for practical applications. By utilizing a key idea of transitioning from graph representation to spectral representation, our approach is generalizable to cold-start nodes, capturing the global structure of the graph without relying on adjacency data. Experimental results demonstrate that our method outperforms existing models on cold-start nodes across various tasks like node classification, node clustering, and link prediction. G-SPARC provides a breakthrough built-in solution to the cold-start problem in graph learning. Our code will be publicly available upon acceptance.

翻译：图在建模各领域复杂关系中发挥着核心作用。大多数图学习方法严重依赖邻域信息，这引发了如何处理冷启动节点——即在图中无已知连接的节点——的问题。现有模型常忽略冷启动节点，导致其在实际场景中失效。为解决此问题，我们提出G-SPARC，一种利用可泛化谱嵌入处理冷启动节点的新型框架。该框架可扩展至前沿方法，使其适用于实际应用。通过采用从图表示转换到谱表示的核心思想，我们的方法能泛化至冷启动节点，在不依赖邻接数据的情况下捕捉图的全局结构。实验结果表明，在节点分类、节点聚类和链接预测等多项任务中，我们的方法对冷启动节点的处理效果优于现有模型。G-SPARC为图学习中的冷启动问题提供了突破性的内置解决方案。我们的代码将在论文录用后公开。