LEGO-Learn: Label-Efficient Graph Open-Set Learning

Understand

How can we train graph-based models to recognize unseen classes while keeping labeling costs low? Graph open-set learning (GOL) and out-of-distribution (OOD) detection aim to address this challenge by training models that can accurately classify known, in-distribution (ID) classes while identifying and handling previously unseen classes during inference.

• It is critical for high-stakes, real-world applications where models frequently encounter unexpected data, including finance, security, and healthcare.
• However, current GOL methods assume access to many labeled ID samples, which is unrealistic for large-scale graphs due to high annotation costs.
• In this paper, we propose LEGO-Learn (Label-Efficient Graph Open-set Learning), a novel framework that tackles open-set node classification on graphs within a given label budget by selecting the most informative ID nodes.