The paper proposes a joint embedding predictive graph SSL framework with GMM-based pseudo-label regularization that reports state-of-the-art node classification accuracy on several benchmark datasets.
Beyond real-world benchmark datasets: An empirical study of node classification with gnns
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Predict, Cluster, Refine: A Joint Embedding Predictive Self-Supervised Framework for Graph Representation Learning
The paper proposes a joint embedding predictive graph SSL framework with GMM-based pseudo-label regularization that reports state-of-the-art node classification accuracy on several benchmark datasets.