A Survey on Deep Graph Generation: Methods and Applications

• URL: http://arxiv.org/abs/2203.06714v1
• Date: Sun, 13 Mar 2022 17:11:43 GMT
• Title: A Survey on Deep Graph Generation: Methods and Applications
• Authors: Yanqiao Zhu and Yuanqi Du and Yinkai Wang and Yichen Xu and Jieyu Zhang and Qiang Liu and Shu Wu
• Abstract summary: Graph generation, whose purpose is to generate new graphs from a distribution similar to the observed graphs, has received increasing attention thanks to the recent advances of deep learning models. We conduct a comprehensive review on the existing literature of graph generation from a variety of emerging methods to its wide application areas.
• Score: 22.713801558059213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graphs are ubiquitous in encoding relational information of real-world objects in many domains. Graph generation, whose purpose is to generate new graphs from a distribution similar to the observed graphs, has received increasing attention thanks to the recent advances of deep learning models. In this paper, we conduct a comprehensive review on the existing literature of graph generation from a variety of emerging methods to its wide application areas. Specifically, we first formulate the problem of deep graph generation and discuss its difference with several related graph learning tasks. Secondly, we divide the state-of-the-art methods into three categories based on model architectures and summarize their generation strategies. Thirdly, we introduce three key application areas of deep graph generation. Lastly, we highlight challenges and opportunities in the future study of deep graph generation.

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