39 citations · 84 across the 9 of their papers we have counts for
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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…
cs.LG2021★ 7 cited
A Tunable Model for Graph Generation Using LSTM and Conditional VAE
Shohei Nakazawa, Yoshiki Sato, Kenji Nakagawa +2
With the development of graph applications, generative models for graphs have been more crucial. Classically, stochastic models that generate graphs with a pre-defined probability…