Web: http://arxiv.org/abs/2201.11494

Jan. 28, 2022, 2:11 a.m. | Shohei Nakazawa, Yoshiki Sato, Sho Tsugawa, Kenji Nakagawa, Kohei Watabe

cs.LG updates on arXiv.org arxiv.org

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-world graphs has gradually attracted the attention of many
researchers. Several generative models that utilize modern machine learning
technologies have been proposed, though a conditional generation of general
graphs is less explored in the field. In this paper, we propose a generative
model that allows us to tune a value of a global-level structural feature as …

arxiv features graph learning model

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