Graph Matching Relaxations and Amortization for Supervised Graph Prediction

Federico Méndez, Paul Krzakala, Gabriel Melo +3

End-to-end Supervised Graph Prediction (SGP) requires a permutation-invariant loss to compare predicted and target graphs with arbitrary node orderings. Such losses typically invol…