Thus,hypergraph-guided embedding can capture functional relations in learned representations.

This paper presents Jigsaw, a novel framework for assembling physically broken 3D objects from multiple pieces.

Graph contrastivelearning (GCL), by training GNNs to maximize the correspondence between the representations of the same graph in its different augmented forms, may yield robust and transferable GNNs even without using labels.

Wethenseparate theoptimization process into two steps, corresponding to weight update and structure parameter update. For the former step, we use the conventional chain rule, which can be sparse via exploiting the sparse structure.