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Besides, label utilization has been proved to be significant factor for node classification problem. Our results show that SeeGera can compare favorably against other state-of-the-art GCL methods in a variety of unsupervised and supervised learning tasks. We propose a novel two-stage constrained actor-critic method: At stage one, we learn individual policies to optimize each auxiliary signal. By utilizing this unified trainer, CogDL can optimize the GNN training loop with several training techniques, such as mixed precision training.