Theoretically, we prove that SFB can learn an asymptotically-optimal predictor without test-domain labels. Empirically, we demonstrate the effectiveness of SFB on real and synthetic data.

In this work, we ask two key questions. First, in a more realistic setting where only a CPU is available, is the GNN model still competitive?

The Probabilistic Watershed isasemi-supervised learning algorithm applied on undirected graphs.