While Positive-Unlabeled (PU) learning is vital in many real-world scenarios,
its application to graph data still remains under-explored. We unveil that a
critical challenge for PU learning on graph lies on the edge heterophily, which
directly violates the irreducibility assumption for Class-Prior Estimation
(class prior is essential for building PU learning algorithms) and degenerates
the latent label inference on unlabeled nodes during classifier training. In
response to this challenge, we introduce a new method, named Graph PU Learning
with Label Propagation Loss (GPL). Specifically, GPL considers learning from PU
nodes along with an intermediate heterophily reduction, which helps mitigate
the negative impact of the heterophilic structure. We formulate this procedure
as a bilevel optimization that reduces heterophily in the inner loop and
efficiently learns a classifier in the outer loop. Extensive experiments across
a variety of datasets have shown that GPL significantly outperforms baseline
methods, confirming its effectiveness and superiority.

图上正无标（PU）学习面临着边异质性的重要挑战，我们提出了一种名为图上 PU 学习与标签传播损失（GPL）的新方法，它考虑了 PU 节点的学习以及中间异质性的减少，以减轻异质结构的负面影响，并在内部循环中降低异质度，在外部循环中高效地学习分类器。大量实验表明，GPL 显著优于基线方法，证实了其有效性和优越性。