Paper Info
Title
A Survey of Community Search Over Big Graphs.
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.
Year
DOI
Venue
2019
10.1007/s00778-019-00556-x
The VLDB Journal
Keywords
Field
DocType
Community search, Community retrieval, Big graph, Graph queries, Online queries
Data science,Community search,Graph,Large networks,Social media,Information technology,Computer science,Big data,Database
Journal
Volume
Issue
ISSN
29
1
1066-8888
Citations 
PageRank 
References 
18
0.59
0
Authors
7
Name
Order
Citations
PageRank
Yixiang Fang122723.06
Xin Huang237929.42
Lu Qin3143095.44
Ying Zhang4128890.39
Wenjie Zhang51616105.67
Reynold Cheng63069154.13
Xuemin Lin75585307.32