用谱嵌入解决图学习中新节点无连接的冷启动问题
SPARC: Spectral Architectures Tackling the Cold-Start Problem in Graph Learning
- 引入通用谱嵌入，无需邻接信息即可预测新节点
- 在节点分类等任务中显著提升冷启动节点性能
- 适合频繁新增节点的现实场景，如社交网络、推荐系统
图在建模数据复杂关系中起核心作用，但现有图学习方法在面对冷启动节点（缺乏初始连接的新节点）时表现不佳，因其依赖邻接信息。为此，我们提出SPARC框架，通过通用谱嵌入实现图学习的新范式。该框架仅需简单增强，即可让先进模型有效预测冷启动节点。通过推理阶段无需邻接信息，并有效捕捉图结构，使模型适用于新节点频繁出现的真实场景。实验表明，SPARC在节点分类、节点聚类和链接预测等任务中，对冷启动节点的性能均优于现有模型，为图学习中的冷启动问题提供了有效解决方案。
原文摘要 · Abstract (English)
Graphs play a central role in modeling complex relationships in data, yet most graph learning methods falter when faced with cold-start nodes--new nodes lacking initial connections--due to their reliance on adjacency information. To tackle this, we propose SPARC, a groundbreaking framework that introduces a novel approach to graph learning by utilizing generalizable spectral embeddings. With a simple yet powerful enhancement, SPARC empowers state-of-the-art methods to make predictions on cold-start nodes effectively. By eliminating the need for adjacency information during inference and effectively capturing the graph's structure, we make these methods suitable for real-world scenarios where new nodes frequently appear. Experimental results demonstrate that our framework outperforms existing models on cold-start nodes across tasks such as node classification, node clustering, and link prediction. SPARC provides a solution to the cold-start problem, advancing the field of graph learning.
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