With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently, a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus, it is desirable to have a comprehensive review of these works. In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze and compare the quality of communities under their models, and the performance of different solutions. Furthermore, we point out new research directions. This survey does not only help researchers to have better understanding of existing community search solutions, but also provides practitioners a better judgment on choosing the proper solutions.

中文翻译：

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大型图社区搜索调查

随着信息技术的飞速发展，各种各样的大图在许多实际应用（例如社交媒体和知识库）中盛行。这些图的重要组成部分是网络社区。本质上，一个社区是一组内部紧密连接的顶点。社区检索可用于许多实际应用程序中，例如事件组织，朋友推荐等。因此，如何有效地从大图中找到高质量的社区是大数据时代的重要研究课题。最近，已经提出了称为社区搜索的大量研究工作。他们旨在为从大型网络实时搜索高质量社区提供有效的解决方案。不过，这些作品着眼于不同类型的图并以不同的方式表述社区，因此，希望对这些作品进行全面的回顾。在这项调查中，我们对现有的社区搜索工作进行了彻底的审查。此外，我们在其模型下分析和比较社区的质量，以及不同解决方案的性能。此外，我们指出了新的研究方向。这项调查不仅有助于研究人员更好地了解现有的社区搜索解决方案，而且还为从业人员在选择合适的解决方案时提供了更好的判断。我们在其模型下分析和比较社区的质量，以及不同解决方案的性能。此外，我们指出了新的研究方向。这项调查不仅有助于研究人员更好地了解现有的社区搜索解决方案，而且还为从业人员在选择合适的解决方案时提供了更好的判断。我们在其模型下分析和比较社区的质量，以及不同解决方案的性能。此外，我们指出了新的研究方向。这项调查不仅有助于研究人员更好地了解现有的社区搜索解决方案，而且还为从业人员在选择合适的解决方案时提供了更好的判断。