Discovering communities in a network, known as community detection/discovery, is a fundamental problem in network science, which attracted much attention in the past several decades. In recent years[when?], with the tremendous studies on big data, another related but different problem, called community search, which aims to find the most likely community that contains the query node, has attracted great attention from both academic and industry areas. It is a query-dependent variant of the community detection problem. A detailed survey of community search can be found at ref.,[1] which reviews all the recent studies
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^Fang, Yixiang; Huang, Xin; Qin, Lu; Zhang, Ying; Zhang, Wenjie; Cheng, Reynold; Lin, Xuemin (2019). "A Survey of Community Search over Big Graphs". arXiv:1904.12539 [cs.DB].
^Sozio, Mauro; Gionis, Aristides (2010). "The community-search problem and how to plan a successful cocktail party". Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10. p. 939. doi:10.1145/1835804.1835923. ISBN9781450300551. S2CID11484255.
^Cui, Wanyun; Xiao, Yanghua; Wang, Haixun; Lu, Yiqi; Wang, Wei (2013). "Online search of overlapping communities". Proceedings of the 2013 international conference on Management of data - SIGMOD '13. p. 277. doi:10.1145/2463676.2463722. ISBN9781450320375. S2CID953025.
^Cui, Wanyun; Xiao, Yanghua; Wang, Haixun; Wang, Wei (2014). "Local search of communities in large graphs". Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. pp. 991–1002. doi:10.1145/2588555.2612179. ISBN9781450323765. S2CID4653380.
^Huang, Xin; Cheng, Hong; Qin, Lu; Tian, Wentao; Yu, Jeffrey Xu (2014). "Querying k-truss community in large and dynamic graphs". Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. pp. 1311–1322. doi:10.1145/2588555.2610495. ISBN9781450323765. S2CID207211829.
^Barbieri, Nicola; Bonchi, Francesco; Galimberti, Edoardo; Gullo, Francesco (2015). "Efficient and effective community search". Data Mining and Knowledge Discovery. 29 (5): 1406–1433. doi:10.1007/s10618-015-0422-1. S2CID13440433.
^Huang, Xin; Lakshmanan, Laks V. S.; Yu, Jeffrey Xu; Cheng, Hong (2015). "Approximate closest community search in networks". Proceedings of the VLDB Endowment. 9 (4): 276–287. arXiv:1505.05956. doi:10.14778/2856318.2856323. S2CID2905457.
^Fang, Yixiang; Cheng, Reynold; Luo, Siqiang; Hu, Jiafeng (2016). "Effective community search for large attributed graphs". Proceedings of the VLDB Endowment. 9 (12): 1233–1244. doi:10.14778/2994509.2994538. hdl:10722/232839.
^Fang, Yixiang; Cheng, Reynold; Li, Xiaodong; Luo, Siqiang; Hu, Jiafeng (2017). "Effective community search over large spatial graphs". Proceedings of the VLDB Endowment. 10 (6): 709–720. doi:10.14778/3055330.3055337. hdl:10722/243528.