A survey of community search over big graphs

Yixiang Fang*, Xin Huang, Lu Qin, Ying Zhang, Wenjie Zhang, Reynold Cheng, Xuemin Lin

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

165 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)353-392
Number of pages40
JournalVLDB Journal
Issue number1
Publication statusPublished - 1 Jan 2020

Scopus Subject Areas

  • Information Systems
  • Hardware and Architecture

User-Defined Keywords

  • Big graph
  • Community retrieval
  • Community search
  • Graph queries
  • Online queries


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