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GraphTune: A Learning-based Graph Generative Model with Tunable Structural Features. (arXiv:2201.11494v1 [cs.LG])
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 …
More from arxiv.org / cs.LG updates on arXiv.org
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