Preprint
Aug 2026
Differentiable Lifting for Topological Neural Networks
This work proposes $\partial$lift (DiffLift), a general framework for learning graph liftings to hypergraphs and cellular- and simplicial complexes in an end-to-end fashion and shows that $\partial$lift outperforms existing lifting methods on multiple benchmarks for graph and node classification across different TNN architectures.
J. L. Franco, Gabriel Duarte, Alexander Nikitin et al.
· 2 citations