6 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 1 cited
An Accurate Graph Generative Model with Tunable Features
Takahiro Yokoyama, Yoshiki Sato, Sho Tsugawa +1
A graph is a very common and powerful data structure used for modeling communication and social networks. Models that generate graphs with arbitrary features are important basic te…
cs.LG2022★ 6 cited
GraphTune: A Learning-based Graph Generative Model with Tunable Structural Features
Kohei Watabe, Shohei Nakazawa, Yoshiki Sato +2
Generative models for graphs have been actively studied for decades, and they have a wide range of applications. Recently, learning-based graph generation that reproduces real-worl…