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General graph generators: experiments, analyses, and improvements

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Graph simulation is one of the most fundamental problems in graph processing and analytics. It can help users to generate new graphs on different scales to mimic observed real-life graphs in many applications such as social networks, biology networks, and information technology. In this paper, we focus on one of the most important types of graph generators: general graph generators, which aim to reproduce the properties of the observed graphs regardless of the domains. Though a variety of graph generators have been proposed in the literature, there are still several important research gaps in this area. In this paper, we first give an overview of the existing general graph generators, including recently emerged deep learning-based approaches. We classify them into four categories: simple model-based generators, complex model-based generators, autoencoder-based generators, and GAN-based generators. Then we conduct a comprehensive experimental evaluation of 20 representative graph generators based on 17 evaluation metrics and 12 real-life graphs. We provide a general roadmap of recommendations for how to select general graph generators under different settings. Furthermore, we propose a new method that can achieve a good trade-off between simulation quality and efficiency. To help researchers and practitioners apply general graph generators in their applications or make a comprehensive evaluation of their proposed general graph generators, we also implement an end-to-end platform that is publicly available.

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Change history

  • 22 November 2021

    The original version was revised due to update in sixth author affiliation.


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This work was supported by 2018YFB2100801, NSFC62102287, 19511101300. Ying Zhang is supported by FT170100128 and ARC DP210101393. Lu Qin is supported by ARC FT200100787. Xuemin Lin is supported by NSFC61232006, 2018YFB1003504, ARC DP200101338 and ARC DP180103096.

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Correspondence to Dawei Cheng.

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Xiang, S., Wen, D., Cheng, D. et al. General graph generators: experiments, analyses, and improvements. The VLDB Journal (2021).

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  • Graph generator
  • Graph neural networks
  • Graph simulation
  • Experimental evaluation