Contextual community search over large social networks

Conference item


Chen, Lu, Liu, Chengfei, Liao, Kewen, Li, Jianxin and Zhou, Rui. (2019). Contextual community search over large social networks. 35th International Conference on Data Engineering (ICDE). Macao, China: IEEE Computer Society. pp. 88 - 99 https://doi.org/10.1109/ICDE.2019.00017
AuthorsChen, Lu, Liu, Chengfei, Liao, Kewen, Li, Jianxin and Zhou, Rui
Abstract

Community search on attributed networks has recently attracted great deal of research interest. However, most of existing works require query users to specify some community structure parameters. This may not be always practical as sometimes a user does not have the knowledge and experience to decide the suitable parameters. In this paper, we propose a novel parameter-free contextual community model for attributed community search. The proposed model only requires a query context, i.e., a set of keywords describing the desired matching community context, while the community returned is both structure and attribute cohesive w.r.t. the provided query context. We theoretically show that both our exact and approximate contextual community search algorithms can be executed in worst case polynomial time. The exact algorithm is based on an elegant parametric maximum flow technique and the approximation algorithm that significantly improves the search efficiency is analyzed to have an approximation factor of 1/3. In the experiment, we use six real networks with ground-truth communities to evaluate the effectiveness of our contextual community model. Experimental results demonstrate that the proposed model can find near ground-truth communities. We also test both our exact and approximate algorithms using eight large real networks to demonstrate the high efficiency of the proposed algorithms.

Year2019
Journal2019 IEEE 35th International Conference on Data Engineering (ICDE)
PublisherIEEE Computer Society
ISSN2375-026X
Digital Object Identifier (DOI)https://doi.org/10.1109/ICDE.2019.00017
Publisher's version
File Access Level
Controlled
Page range88 - 99
ISBN9781538674741
Research GroupPeter Faber Business School
Place of publicationMacao, China
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