Improving Graph Generation by Restricting Graph Bandwidth
- URL: http://arxiv.org/abs/2301.10857v2
- Date: Tue, 30 May 2023 22:56:34 GMT
- Title: Improving Graph Generation by Restricting Graph Bandwidth
- Authors: Nathaniel Diamant, Alex M. Tseng, Kangway V. Chuang, Tommaso
Biancalani, Gabriele Scalia
- Abstract summary: Deep graph generative modeling has proven capable of learning the distribution of complex, multi-scale structures characterizing real-world graphs.
One of the main limitations of existing methods is their large output space.
We propose a novel approach that significantly reduces the output space of existing graph generative models.
- Score: 0.7874708385247353
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deep graph generative modeling has proven capable of learning the
distribution of complex, multi-scale structures characterizing real-world
graphs. However, one of the main limitations of existing methods is their large
output space, which limits generation scalability and hinders accurate modeling
of the underlying distribution. To overcome these limitations, we propose a
novel approach that significantly reduces the output space of existing graph
generative models. Specifically, starting from the observation that many
real-world graphs have low graph bandwidth, we restrict graph bandwidth during
training and generation. Our strategy improves both generation scalability and
quality without increasing architectural complexity or reducing expressiveness.
Our approach is compatible with existing graph generative methods, and we
describe its application to both autoregressive and one-shot models. We
extensively validate our strategy on synthetic and real datasets, including
molecular graphs. Our experiments show that, in addition to improving
generation efficiency, our approach consistently improves generation quality
and reconstruction accuracy. The implementation is made available.
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