The abundance of interconnected data has fueled the design and implementation of graph generators reproducing real-world linking properties or gauging the effectiveness of graph algorithms, techniques, and applications manipulating these data. We consider graph generation across multiple subfields, such as Semantic Web, graph databases, social networks, and community detection, along with general graphs. Despite the disparate requirements of modern graph generators throughout these communities, we analyze them under a common umbrella, reaching out the functionalities, the practical usage, and their supported operations. We argue that this classification is serving the need of providing scientists, researchers, and practitioners with the right data generator at hand for their work. This survey provides a comprehensive overview of the state-of-the-art graph generators by focusing on those that are pertinent and suitable for several data-intensive tasks. Finally, we discuss open challenges and missing requirements of current graph generators along with their future extensions to new emerging fields.

中文翻译：

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图形生成器

大量的互连数据推动了图形生成器的设计和实现，这些生成器再现了现实世界的链接属性，或者衡量了处理这些数据的图形算法、技术和应用程序的有效性。我们考虑跨多个子领域的图生成，例如语义网、图数据库、社交网络和社区检测，以及通用图。尽管这些社区中现代图形生成器的要求不同，但我们在一个共同的保护伞下对它们进行了分析，涵盖了功能、实际用途及其支持的操作。我们认为，这种分类满足了为科学家、研究人员和从业人员提供适合他们工作的手头数据生成器的需求。本调查通过关注那些相关且适用于多个数据密集型任务的图形生成器，全面概述了最先进的图形生成器。最后，我们讨论了当前图形生成器的开放挑战和缺失的需求，以及它们未来对新兴领域的扩展。