2025
MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy
ICLR 2025poster
Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy function of interest. In this way, node embeddings prod…