  Detecting illicit nodes on blockchain networks is a valuable task for
strengthening future regulation. Recent machine learning-based methods proposed
to tackle the tasks are using some blockchain transaction datasets with a small
portion of samples labeled positive and the rest unlabelled (PU). Albeit the
assumption that a random sample of unlabeled nodes are normal nodes is used in
some works, we discuss that the label mechanism assumption for the hidden
positive labels and its effect on the evaluation metrics is worth considering.
We further explore that PU classifiers dealing with potential hidden positive
labels can have improved performance compared to regular machine learning
models. We test the PU classifiers with a list of graph representation learning
methods for obtaining different feature distributions for the same data to have
more reliable results.
