ROG$_{PL}$: Robust Open-Set Graph Learning via Region-Based Prototype Learning

arXiv:2402.18495v1 Announce Type: new
Abstract: Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open-set scenarios due to the complex data they encounter, such as out-of-distribution (OOD) data and in-distribution (IND) noise. OOD data are samples that do not belong to any known classes. They are outliers if they occur in training (OOD noise), and open-set samples if …

abstract arxiv class classification cs.lg data distribution graph graph learning identify node nodes practical robust samples set type via