  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 they occur in testing. IND noise are training
samples which are assigned incorrect labels. The existence of IND noise and OOD
noise is prevalent, which usually cause the ambiguity problem, including the
intra-class variety problem and the inter-class confusion problem. Thus, to
explore robust open-set learning methods is necessary and difficult, and it
becomes even more difficult for non-IID graph data.To this end, we propose a
unified framework named ROG$_{PL}$ to achieve robust open-set learning on
complex noisy graph data, by introducing prototype learning. In specific,
ROG$_{PL}$ consists of two modules, i.e., denoising via label propagation and
open-set prototype learning via regions. The first module corrects noisy labels
through similarity-based label propagation and removes low-confidence samples,
to solve the intra-class variety problem caused by noise. The second module
learns open-set prototypes for each known class via non-overlapped regions and
remains both interior and border prototypes to remedy the inter-class confusion
problem.The two modules are iteratively updated under the constraints of
classification loss and prototype diversity loss. To the best of our knowledge,
the proposed ROG$_{PL}$ is the first robust open-set node classification method
for graph data with complex noise.
