VAC: Vertex-Centric Attributed Community Search

• Published in: 2020 IEEE 36th International Conference on Data Engineering (ICDE) pp. 937 - 948
• Main Authors: Liu, Qing, Zhu, Yifan, Zhao, Minjun, Huang, Xin, Xu, Jianliang, Gao, Yunjun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2020
• Subjects: Approximation algorithms; Complex networks; Computer science; Conferences; Euclidean distance; Search problems
• ISSN: 2375-026X
• DOI: 10.1109/ICDE48307.2020.00086

Attributed community search aims to find the community with strong structure and attribute cohesiveness from attributed graphs. However, existing works suffer from two major limitations: (i) it is not easy to set the conditions on query attributes; (ii) the queries support only a single type of attributes. To make up for these deficiencies, in this paper, we study a novel attributed community search called vertex-centric attributed community (VAC) search. Given an attributed graph and a query vertex set, the VAC search returns the community which is densely connected (ensured by the k-truss model) and has the best attribute score. We show that the problem is NP-hard. To answer the VAC search, we develop both exact and approximate algorithms. Specifically, we develop two exact algorithms. One searches the community in a depth-first manner and the other is in a best-first manner. We also propose a set of heuristic strategies to prune the unqualified search space by exploiting the structure and attribute properties. In addition, to further improve the search efficiency, we propose a 2-approximation algorithm. Comprehensive experimental studies on various realworld attributed graphs demonstrate the effectiveness of the proposed model and the efficiency of the developed algorithms.