Graph neural networks have pushed state-of-the-arts in graph classifications
recently. Typically, these methods are studied within the context of supervised
end-to-end training, which necessities copious task-specific labels. However,
in real-world circumstances, labeled data could be limited, and there could be
a massive corpus of unlabeled data, even from unknown classes as a
complementary. Towards this end, we study the problem of semi-supervised
universal graph classification, which not only identifies graph samples which
do not belong to known classes, but also classifies the remaining samples into
their respective classes. This problem is challenging due to a severe lack of
labels and potential class shifts. In this paper, we propose a novel graph
neural network framework named UGNN, which makes the best of unlabeled data
from the subgraph perspective. To tackle class shifts, we estimate the
certainty of unlabeled graphs using multiple subgraphs, which facilities the
discovery of unlabeled data from unknown categories. Moreover, we construct
semantic prototypes in the embedding space for both known and unknown
categories and utilize posterior prototype assignments inferred from the
Sinkhorn-Knopp algorithm to learn from abundant unlabeled graphs across
different subgraph views. Extensive experiments on six datasets verify the
effectiveness of UGNN in different settings.

本文提出一种新型的图神经网络框架 UGNN，用于半监督图分类，在有限标注数据和大量未标注数据的情况下，利用子图的视角来充分利用未标注数据，并且通过使用 Sinkhorn-Knopp 算法来学习已知和未知类别的语义原型，并在不同的子图视图下进行大量实验，证明了 UGNN 在不同设定下的有效性。