Open-World Lifelong Graph Learning
Marcel Hoffmann Lukas Galke Ansgar Scherp
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.