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

NP$^2$L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks