  How to utilize the pseudo labels has always been a research hotspot in
machine learning. However, most methods use pseudo labels as supervised
training, and lack of valid assessing for their accuracy. Moreover,
applications of pseudo labels in graph neural networks (GNNs) oversee the
difference between graph learning and other machine learning tasks such as
message passing mechanism. Aiming to address the first issue, we found through
a large number of experiments that the pseudo labels are more accurate if they
are selected by not overlapping partial labels and defined as negative node
pairs relations. Therefore, considering the extraction based on pseudo and
partial labels, negative edges are constructed between two nodes by the
negative pseudo partial labels extraction (NP$^2$E) module. With that, a signed
graph are built containing highly accurate pseudo labels information from the
original graph, which effectively assists GNN in learning at the
message-passing level, provide one solution to the second issue. Empirical
results about link prediction and node classification tasks on several
benchmark datasets demonstrate the effectiveness of our method.
State-of-the-art performance is achieved on the both tasks.

Machine Learning;Artificial Intelligence;Social and Information Networks