  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 a
better understanding of existing community search solutions, but also provides
practitioners a better judgment on choosing the proper solutions.
