Approximation Ratios of Graph Neural Networks for Combinatorial Problems

NeurIPS 2019 Ryoma SatoMakoto YamadaHisashi Kashima

In this paper, from a theoretical perspective, we study how powerful graph neural networks (GNNs) can be for learning approximation algorithms for combinatorial problems. To this end, we first establish a new class of GNNs that can solve a strictly wider variety of problems than existing GNNs... (read more)

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