Approximation Ratios of Graph Neural Networks for Combinatorial Problems

Ryoma Sato, Makoto Yamada, Hisashi Kashima

Neural Information Processing Systems 

To this end, we first establish a new class of GNNs that can solve a strictly wider variety of problems than existing GNNs. Then, we bridge the gap between GNN theory and the theory of distributed local algorithms.

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