Related papers: Open-World Lifelong Graph Learning

Open-World Lifelong Graph Learning

URL: http://arxiv.org/abs/2310.12565v1
Date: Thu, 19 Oct 2023 08:18:10 GMT
Title: Open-World Lifelong Graph Learning
Authors: Marcel Hoffmann, Lukas Galke, Ansgar Scherp
Abstract summary: We study the problem of lifelong graph learning in an open-world scenario. We utilize Out-of-Distribution (OOD) detection methods to recognize new classes. We suggest performing new class detection by combining OOD detection methods with information aggregated from the graph neighborhood.
Score: 7.535219325248997
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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