2024
From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module
Claudio Battiloro, Indro Spinelli, Lev Telyatnikov, Michael M. Bronstein, Simone Scardapane, Paolo Di Lorenzo
ICLR 2024poster
Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networks (GNNs) on a given graph topology by dynamically learning it. However, most of LGI methods assume to have a (noisy, incomplete, improvable, ...) input graph to rewire and can solely learn regular graph topologies. In the wake…