83 citations · 160 across the 26 of their papers we have counts for
3 papers · 1 filter
Approximation Ratios of Graph Neural Networks for Combinatorial Problems
Ryoma Sato, Makoto Yamada, Hisashi 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 e…
Learning to Sample Hard Instances for Graph Algorithms
Ryoma Sato, Makoto Yamada, Hisashi Kashima
Hard instances, which require a long time for a specific algorithm to solve, help (1) analyze the algorithm for accelerating it and (2) build a good benchmark for evaluating the pe…
Constant Time Graph Neural Networks
Ryoma Sato, Makoto Yamada, Hisashi Kashima
The recent advancements in graph neural networks (GNNs) have led to state-of-the-art performances in various applications, including chemo-informatics, question-answering systems,…