We study the problem of lifelong graph learning in an open-world scenario,
where a model needs to deal with new tasks and potentially unknown classes. We
utilize Out-of-Distribution (OOD) detection methods to recognize new classes
and adapt existing non-graph OOD detection methods to graph data. Crucially, we
suggest performing new class detection by combining OOD detection methods with
information aggregated from the graph neighborhood. Most OOD detection methods
avoid determining a crisp threshold for deciding whether a vertex is OOD. To
tackle this problem, we propose a Weakly-supervised Relevance Feedback
(Open-WRF) method, which decreases the sensitivity to thresholds in OOD
detection. We evaluate our approach on six benchmark datasets. Our results show
that the proposed neighborhood aggregation method for OOD scores outperforms
existing methods independent of the underlying graph neural network.
Furthermore, we demonstrate that our Open-WRF method is more robust to
threshold selection and analyze the influence of graph neighborhood on OOD
detection. The aggregation and threshold methods are compatible with arbitrary
graph neural networks and OOD detection methods, making our approach versatile
and applicable to many real-world applications.

我们在开放世界场景中研究了终身图形学习的问题，通过利用 ODD 检测方法来识别新的类别，并将现有的非图形 ODD 检测方法应用于图形数据中，通过聚合图形邻域信息与 OOD 检测方法相结合来进行新类别的检测。我们提出了一种弱监督相关反馈（Open-WRF）方法，用于减少 OOD 检测中阈值的敏感性，并通过在六个基准数据集上的评估结果证明我们的方法在 OOD 评分的邻域聚合上的性能优于现有方法，同时我们证明了我们的 Open-WRF 方法在阈值选择上更为鲁棒，并分析了图形邻域对 OOD 检测的影响。我们的聚合和阈值方法与任意图形神经网络和 OOD 检测方法兼容，使得我们的方法适用于许多实际应用。