  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.
