In real-world graph data, distribution shifts can manifest in various ways, such as the emergence of new categories and changes in the relative proportions of existing categories. It is often important to detect nodes of novel categories under such distribution shifts for safety or insight discovery purposes. We introduce a new approach, Recall-Constrained Optimization with Selective Link Prediction (RECO-SLIP), to detect nodes belonging to novel categories in attributed graphs under subpopulation shifts. By integrating a recall-constrained learning framework with a sample-efficient link prediction mechanism, RECO-SLIP addresses the dual challenges of resilience against subpopulation shifts and the effective exploitation of graph structure. Our extensive empirical evaluation across multiple graph datasets demonstrates the superior performance of RECO-SLIP over existing methods. The experimental code is available at https://github.com/hsinghuan/novel-node-category-detection.

翻译：在现实世界的图数据中，分布偏移可能以多种方式显现，例如新类别的出现以及现有类别相对比例的变化。出于安全或洞察发现的目的，在此类分布偏移下检测属于新类别的节点通常至关重要。我们提出了一种新方法——结合选择性链接预测的召回约束优化（RECO-SLIP），用于在子群分布偏移下的属性图中检测属于新类别的节点。通过将召回约束学习框架与样本高效的链接预测机制相结合，RECO-SLIP解决了应对子群分布偏移的鲁棒性和有效利用图结构的双重挑战。我们在多个图数据集上进行的大量实证评估表明，RECO-SLIP的性能优于现有方法。实验代码可在 https://github.com/hsinghuan/novel-node-category-detection 获取。