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A survey of community search over big graphs

  • Yixiang FangEmail author
  • Xin Huang
  • Lu Qin
  • Ying Zhang
  • Wenjie Zhang
  • Reynold Cheng
  • Xuemin Lin
Special Issue Paper
  • 110 Downloads

Abstract

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.

Keywords

Community search Community retrieval Big graph Graph queries Online queries 

Notes

Acknowledgements

We would like to thank Jiafeng Hu and Kai Wang for their helpful discussions, Dan Yin for the proof-reading, and Jinbin Huang for conducting experimental comparisons. Xin Huang is supported by the NSFC Project No. 61702435, and Hong Kong General Research Fund (GRF) Project No. HKBU 12200917. Lu Qin is supported by DP160101513. Ying Zhang is supported by FT170100128 and DP180103096. Wenjie Zhang is supported by DP180103096. Reynold Cheng is supported by the Research Grants Council of Hong Kong (RGC Projects HKU 17229116 and 17205115) and HKU (Projects 102009508 and 104004129). Xuemin Lin is supported by 2019DH0ZX01, 2018YFB1003504, NSFC61232006, DP180103096, and DP170101628.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of New South WalesSydneyAustralia
  2. 2.Zhejiang LabHangzhouChina
  3. 3.Hong Kong Baptist UniversityKowloon TongHong Kong
  4. 4.The University of Technology SydneySydneyAustralia
  5. 5.The University of Hong KongPok Fu LamHong Kong

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